Energy efficiency through optimization of components in a building envelope by Sanandana C M

Achieving energy efficiency through optimization of components of a building envelope A framework towards energy efficient neighbourhoods Episode 2

A research by C M Sanandana Guided by Prof. Lilly Rose A

Achieving energy efficiency through optimization of components of a building envelope A framework towards energy efficient neighbourhoods Episode 2

A research by C M Sanandana Guided by Prof. Lilly Rose A

UNDER GRADUATE PROGRAMME IN ARCHITECTURE STUDENT NAME: C M SANANDANA (UA1517) DRP TITLE : Achieving energy efficiency through optimization of components of a building envelope : A framework towards energy efficient neighbourhoods - Episode 2

APPROVAL The following study is hereby approved as a creditable work on the approved subject carried out and presented in the manner, sufficiently satisfactory to warrant its acceptance as a pre-requisite to the degree of Bachelor of Architecture for which it has been submitted. It is to be understood that by this approval, the undersigned does not endorse or approve the statements made, opinions expressed or conclusion drawn therein, but approves the study only for the purpose for which it has been submitted and satisfies him to the requirements laid down in the academic program.

_______________________________

Signature of the guide

Dean, Faculty of Architecture

Name of the guide: Lilly Rose A

Date :

DECLARATION This work contains no material which has been accepted for the award of any other degree or diploma in any University or other institutions and to the best of my knowledge does not contain any material previously published or written by another person except where due reference has been made in the text. I consent to this copy of DRP, when in the library of CEPT University, being available on loan and photocopying.

Student name : C M Sanandana Date : 2nd May, 2022 Signature

Acknowledgments I express my love and gratitude to my parents without whom I wouldn’t have reached here at this juncture. I would also like to mention my sister and Puru, for always believing in me. I also take this moment to remember my dear grandmother, whose smile and motivation has always encouraged me. This paper would not have been possible without the constant support and encouragement of my guide, Prof. Lilly Rose A, who has always been there for every small doubt however silly it might have been, and for giving me exposure to various softwares and resources regarding the research. I would also like to mention Ritwik Behuria, the author of episode 1 of this series, for motivating and supporting me all along and for humbly hosting me for the long duration of the on-site data collection in Hyderabad. Speaking of Hyderabad, I would like to thank all the surveyees who generously let two unknown strangers into their apartments and let them gather data from their residences and the neighbourhoods for letting us in during the uncertain times of the pandemic. A special mention to all the people who patiently filled our long survey and gave their valuable inputs to the study. I would like to thank my friends who have always encouraged me to do better throughout the five years of my time spent at CEPT and for sharing very memorable moments with me. I would like to mention Neem apartment and Sundar Shanti, the two spaces where most of this paper was shaped and formulated to fruition. A special mention to the balconies and the insights they offered at times whenever the study hit a road bump. I would like to also thank Ahmedabad and its unbearable heat which really pushed me to think in the direction of energy efficiency and made us realize how real climate change is. I would like to thank everything, tangible and intangible that has led to this point in my life. I express my gratitude to all the people and all other inhabitants of our planet, who motivated me to realize that we are but a tiny part of the delicate eco-system of Earth and we collectively can live in harmony without harming each other. Lastly, I would like to recall the beautiful boulders and the landscapes back home for always inspiring me every time I go there.

A study on energy efficiency This paper is part of a two-episode series which attempts to look at energy efficient neighbourhoods in the context of India through a case study of certain localities in Hyderabad.

Episode 1

Macro-scale approach towards enhancing thermal comfort in residential neighbourhoods Research Questions:

• What extent of energy efficiency can be achieved in high, mid and lowrise residential households through macro-scale optimization in design stages? • What impact can this neighborhood-level planning (massing, zoning and climatically appropriate orientation) have on high-rise residential built forms when compared to mid-rise or low-rise residential built forms?

Episode 2 (Current episode)

Achieving energy efficiency through optimization of components of a building envelope Research Questions:

• What are the components in a building’s envelope which affect the thermal and visual comfort of its user group? • What is the contribution of a building envelope to its energy consumption? How do its various components behave in different scales of typologies? • How can the energy efficiency of residential neighbourhoods be optimized through parameters and digital simulations?

Contents Chapter 1

Overview

1.1 Introduction 1.2 Aim 1.3 Objectives 1.4 Research Questions 1.5 Scope 1.6 Limitations 1.7 Methodology Chapter 2

Background 2.1 Need for energy efficiency 2.2 Building envelope & energy efficiency 2.2.1 WWR_Window to wall ratio 2.2.1 a WWR and energy consumption 2.2.1 b WWR and orientation 2.2.2 Fenestration 2.2.3 Envelope properties 2.2.3 a Wall 2.2.3 b Roof 2.2.4 Facade treatment 2.2.4 a Horizontal Overhangs 2.2.4 b WWR and horizontal shading devices 2.2.4 c Other shading devices 2.2.4 d Density and porosity of facades

Chapter 3

Research Methodology

3.1 Area of study 3.2 A study of neighbourhoods 3.3 Local climate zone classification 3.4 Neighbourhoods selected 3.5 Hyderabad and its climate 3.6 Parameters of study 3.7 Simulations 3.7.1 Overview of the analysis 3.7.2 Overview of the simulation 3.7.3 Modelling for simulation 3.7.4 Conditions for iterations

Chapter 4

Analysis 4.1 Low-rise neighbourhood 4.1.1 About the neighbourhood 4.1.2 LCZ classification 4.1.3 User survey and data collection 4.1.4 Inferences from the survey 4.1.4 a Thermal comfort 4.1.4 b Air movement 4.1.4 c Daylighting 4.1.4 d Energy consumption 4.1.4 e Inference

Chapter 4

Analysis 4.2 Mid-rise neighbourhood 4.2.1 About the neighbourhood 4.2.2 LCZ classification 4.2.3 User survey and data collection 4.2.3 User survey and data collection 4.2.3 a Study of the units 4.2.3 a i. Ground floor unit_Block B1 4.2.3 a ii. Fourth floor unit_Block A 4.2.3 a iii. Top floor unit_Block A 4.2.4 Simulation of the neighbourhood 4.2.4 a Validation of the simulation 4.2.4 b Simulation of the base case 4.2.4 c Iteration 1: Increase in the air-conditioned area 4.2.4 d Iteration 2: Increase in WWR 4.2.4 e Iteration 3: Uniform WWR 4.2.4 f Iteration 4: Increase in the projection factor 4.2.4 g Iteration 5: Uniform projection factor

4.3 High-rise neighbourhood 4.3.1 About the neighbourhood 4.3.2 LCZ classification 4.3.3 User survey and data collection 4.3.3 a User Survey 4.3.3 b Inferences from the survey 4.3.3 b i. Thermal comfort 4.3.3 b ii. Air movement 4.3.3 b iii. Daylighting 4.3.3 b iv. Energy consumption 4.3.3 c Study of the units 4.3.3 c i. Third floor unit_Block M 4.3.4 Simulation of the neighbourhood

Chapter 4

Analysis

4.3 High-rise neighbourhood 4.3.4 a Simulation of the base case 4.3.4 b Iteration 1: Increase in WWR 4.3.4 c Iteration 2: Uniform WWR 4.3.4 d Iteration 3: Increase in the projection factor with context 4.3.4 e Iteration 4: Increase in the projection factor without context

Chapter 5

Conclusion

104

5.1 Comparison of the iterations 5.1.1 Iterations of the mid-rise neighbourhood 5.1.1 a Shading devices in the presence of the surrounding context 5.1.1 b Shading devices in the absence of the surrounding context 5.1.1 c Increase in WWR in the presence of the surrounding context 5.1.1 d Increase in WWR in the absence of the surrounding context 5.1.1 e Inference 5.1.2 Iterations of the high-rise neighbourhood 5.1.2 a Shading devices in the presence of the surrounding context 5.1.2 b Shading devices in the absence of the surrounding context 5.1.2 c Increase in WWR in the presence of the surrounding context 5.1.2 d Increase in WWR in the absence of the surrounding context 5.1.2 e Inference

5.2 Future scope and creation of a framework 5.3 The way ahead

Appendix

126

Bibliography

132

Chapter 1 Overview 1.1 Introduction 1.2 Aim 1.3 Objectives 1.4 Research Questions 1.5 Scope 1.6 Limitations 1.7 Methodology

Go to Contents

1.1 Introduction A large portion of the built landscape in India can be attributed to various stakeholders like developers, engineers, architects and the common public just to list a few. With capital becoming the predominant driving force in the building sector, practices such as conscious use of resources, energy efficiency and regeneration remain confined to a certain section of people and occupy a small portion of the built. As cities in India grow and new urban centers begin to emerge due to unprecedented rates of migration, the building sector would be at the forefront of this growth and could contribute significantly to the environment. A population boom coupled with a relentless desire to build could result in tremendous pressure on land leading to denser cities and neighbourhoods, which in turn could have serious impacts on the environment unless planned with care and concern. Residential buildings account for a significant portion of the built environment and will continue to do so in the decades to come. Over the past few centuries, residential neighbourhoods have undergone various transformations resulting in scales ranging from towering apartments to humble dwellings. As decades roll into the 21st century, with climate and temperatures changing drastically, there is a dire need for collective intervention and a significant shift in the lifestyle of people along with the way buildings and neighbourhoods are built, addressing not just humans but also the environment. Environmentally conscious methods of construction and energy efficient buildings and localities ought to become a part of the mainstream practice in spite of such concepts being largely unknown to a vast majority of people in India. The exponential increase of cooling loads due to temperature rise indicates the urgency of the situation. The recurring heat waves in the past month are a testimony to this fact. The research is part of an inquiry on approaching the goal of net-zero neighbourhoods with respect to resources, waste and energy; in a nutshell, the creation of flourishing built ecosystems which are in equilibrium with their environment. The paper is a continuation of a series which looks at analyzing neighbourhoods in order to achieve energy efficiency. The following study focuses on how one can intervene at the early stages to design environmentally sound and energy efficient building envelopes with the backdrop of residential localities. The study is based on residential neighbourhoods located in Hyderabad, a rapidly developing metropolis of India. The research intends to contribute to the growth of cities so that informed decisions can be made for a healthier future for planet earth and all its inhabitants.

Overview

1.2 Aim The intention is to investigate the implications of components of a building envelope on its energy consumption and analyze them by looking at the typology of residential neighbourhoods in India.

1.3 Objectives • To identify parameters in order to analyze building envelopes of a neighbourhood by breaking it down into the following components : Vertical Surfaces Opaque Surfaces (Surface treatment, material of the wall, window to wall ratio, aspect ratio, albedo values)

Fenestrations (Physical dimensions, nature, materiality, details, operability, shading devices, time of operation, balconies, jaali/any other types of facade treatment) Roof Area (Horizontal surface treatment, material of the slab, area exposed to sun, nature) • To gather data regarding the above mentioned parameters through case studies of neighbourhoods and user surveys to understand their thermal and visual comfort levels and energy consumption patterns • To simulate the collected data digitally using OpenStudio and to assess the most significant parameters and their percentage contribution towards energy efficiency • To analyze the behaviour of these parameters at different scales and hence identify their significance at their respective scales

1.4 Research Questions • What are the components in a building’s envelope which affect the thermal and visual comfort of its user group? • What is the contribution of a building envelope to its energy consumption? How do its various components behave in different scales of typologies? • How can the energy efficiency of residential neighbourhoods be optimized through parameters and digital simulations? What is the percentage change when compared with the existing energy consumption patterns?

1.5 Scope The research looks at analyzing components of a building envelope in order to achieve energy efficiency so that it can inform design decisions in the future. The study looks at choosing certain parameters through a literature review and analyzing them through on-site studies and digital simulations to draw inferences. The findings could then be expanded to other neighbourhoods across India with due changes. The study focuses on the typology of residential neighbourhoods of three different scales set in the composite climate of the metropolis Hyderabad, located in the state of Telangana in India.

Overview

1.6 Limitations • One of the drawbacks of the research is that it takes into consideration the outer envelope of a building while ignoring the interior spaces, which could also play a major role in contributing to its energy consumption. • The entire study has been subject to a short time period of four months along with an ongoing pandemic and the various challenges it poses. • Only a certain portion of the case studies (with a certain amount of detailing) could be simulated digitally due to challenges posed by the software and its simulations. • A lot of neighbourhoods and residences have been formed on ideas and popular beliefs such as the principles of Vaastu which would not be taken into account for the study. • User behaviour, one of the key factors in energy consumption, has not been taken into consideration for the study. • Only two parameters were considered for analysis in the study, while the addition of more parameters could have given a comprehensive result. • The study focuses on a certain type of neighbourhood in Hyderabad (of a certain scale and density - inhabited by people of certain economic strata) while it could have included a diverse set of samplings for better comprehension. In order to expand the study to any other neighbourhood of India, one has to take make note of the same.

1.7 Methodology The following chart illustrates the sequence of the study.

Energy Efficiency Parameter Identification Need for energy efficiency

LITERATURE STUDY Residential Neighbourhoods On-site Data collection

LOCAL CASE STUDIES

Pilot Study

Digital Simulation Testing Iterations

DATA ANALYSIS

Comparison & Analysis Optimization of parameters Findings & Suggestions Inferences

CONCLUSION

Overview

Residential Neighbourhood Typology

Design Parameters

Hyderabad

Low-rise Neighbourhood Mid-rise Neighbourhood

Window to Wall Ratio

High-rise Neighbourhood

Parameters

Envelope Properties

Online User Survey Surface Temperatures Lux Levels

Fenestration

Electricity Consumption

Mid-rise Neighbourhood Hallmark Tranquil, Manikonda, Hyderabad

Shading Devices

Online Survey Selection of a Block Selection of units at different levels

Facade Treatment

Unit-wise analysis

Thermal and Visual Comfort

Chapter 2 Background

2.1

Need for Energy Efficiency

2.2

Building Envelope & Energy Efficiency

2.2.1

WWR

2.2.2

Fenestration

2.2.3

Envelope properties

2.2.4

Facade treatment

Go to Contents

2.1 Need for energy efficiency Global temperatures are increasing every year and is exerting pressure on the need for cooling loads and hence electricity generation. With a majority of the energy being generated from non-renewable resources as seen in Figure 1, it is essential to design energy efficient spaces so as to reduce the dependency on them. Over the years, the domestic sector has been occupying a significant portion of the total energy consumption in India and is on the constant rise. It is predicted that the energy consumption of the domestic sector in India would increase by eight times in the year 2050 (Rawal et al., 2015). Additionally, a surge of about 400% is expected in the aggregate floor area of buildings by 2030 (Kumar, S., 2011). Figure 3 indicates that India is producing more residential units (HIG and MIG) than required; hence, it is essential to design spaces efficiently keeping in mind the use of resources and energy involved in their making and functioning.

Figure 1. Graph showing electricity production through renewable and non-renewable Sources (Energy in Million Units) Source: CEA Annual Report 2019-20 26

Background

Figure 2. Graph showing total electricity consumption v/s domestic electricity consumption in India (Energy in GWh) Source: Growth of electricity sector in India from 1947-2020

Figure 3. Graph showing demand and supply of different housing types: LIG, MIG, HIG in India Source: Cushman & Wakefield (March 13, 2017). Reality Check. India Today. 27

2.2 Building envelope & energy efficiency A building’s envelope separates its users inside from the outside environment. While humans tend to seek protection from the harsh conditions prevalent outside, a balanced and controlled thoroughfare between the inside and the outside is essential; and, the building envelope plays a major role in achieving the same. The following section attempts to look at existing literature regarding various components of a building envelope and their effect on energy consumption.

2.2.1 WWR (Window to wall ratio) WWR is the ratio of the total fenestration area to the gross exterior wall area (User Guide - Bureau of Energy Efficiency, 2011). The ratio indicates the amount of puncture done in an envelope to allow natural light and ventilation into the space. The larger the ratio, the larger the possibility for the exchange of wind, light and heat in the space. Various studies have shown that the building energy consumption and WWR are in a positive linear relationship (Liu et al., 2021). 2.3.1 a WWR and energy consumption A study points out that, an increase in WWR from14% to 56%, leads to a direct increase in the air-conditioning energy consumption from 18.7% to 39.8% (Ma & Ma, 2022). Another study involving office buildings in Sao Paolo shows that there is a reduction of 6% in the Energy Utilization Index (EUI) of buildings with a WWR of 30% when compared to those with a 56% WWR (de Oliveira Neves & Marques, 2017). A study in Cairo shows a significant reduction in electricity consumption with a combination of 30% WWR and a clear, reflective 6.4 thick glass as opposed to a combination of clear glass with 10% WWR (Mahdy & Nikolopoulou, 2014).

2.3.1 b WWR and orientation A research conducted on comparing a WWRs in four cardinal directions of a detached house, a multi-storey building and a high-rise building shows that for the southern facade, a WWR of 45% corresponds to a cooling load of 862.1, 494.3 and 397.0 kWh their energy consumption (per sqm) were 116.8, 108.7 and 100.6 respectively. Similarly, the same WWR on the eastern facade corresponds to a cooling load of 601.6, 365.6 and 509.3 kWh; and their energy consumption per square meter was 180.2, 133.4 and 131.9 kWh respectively (Liu et al., 2021).

Background

2.2.2 Fenestration Fenestrations are an essential part of envelope design. The term fenestration has various connotations attached to it but, for the purpose of the study is used interchangeably with the term windows. A window has various components and each one of it has its own significance. Here, the aspect of glazing is considered for the study. In India, 80% of the total heat flow into buildings is by direct solar radiation on window glazing. The proper design of windows can greatly reduce energy consumption in buildings (Kumar.S, 2012). A residence with just one room of 10 sq m area with concrete construction situated in Delhi, India shows that solar transmission through windows is 10% while the energy contribution through windows is also 10% (Bansal & Mathur, 2006). A study done in Malaysia shows that a reduction of 19% in cooling loads can be achieved by replacing single clear glass (6 mm) with double glazed clear low-e glass (6/12/6 mm) (Al-Ashwal & Hassan, 2015).

2.2.3 Envelope properties The envelope of an orthogonal building has vertical and horizontal surfaces. The surfaces of a wall make up vertical surfaces while elements of a roof or a terrace make up the horizontal surfaces. They conduct heat according to their properties and affect the interiors of a unit, hence affecting its cooling loads. 2.2.3 a Wall Properties Various parameters of a wall, like thickness, thermal properties and surface treatment can yield varying results of energy consumption. A thicker wall facing east and west directions as opposed to the thinner walls is said to decrease their solar radiation gain; by doubling the thickness of the wall, a reduction of around 6% can be achieved in the building’s cooling loads (Wong & Li, 2007). In a hot and dry climatic zone in India, a building made with mud bricks (unburnt) and coupled with bronze reflective glass gains 33.56 kWh while, a building made of dense concrete walls with clear glass windows gains 38.93 kWh of heat through its envelope. However, in a composite climate like that of New Delhi, similar conditions yielded a heat gain of 37.82 kWh and 45.08 kWh respectively. The WWR for the study was taken as 40% (Kumar et al., 2017).

2.2.3 b Roof The roof is one of the envelope components which can bring about a significant reduction in energy consumption through appropriate intervention (Ganguly et al., 2016). Around 11% energy savings can be achieved with the addition of a secondary roof as a thermal buffer (Wong & Li, 2007). According to A. Niachou et. al., the maximum indoor air temperature achieved on an average with a green roof was 29°C while it was 31°C without a green roof. The green roof also showed a lesser fluctuation in the temperature with an average of a 4°C daily range, while it was around 7°C without the green roof. Installation of a green roof showed a decrease in cooling load from 15% to 39% for the entire building; however, the last floor of the building showed a greater reduction percentage of up-to 58% during summers (Spala et al., 2008).

2.2.4 Facade treatment Any additional element or a different treatment done to the facade of a building has been considered under the category of facade treatment in this study. 2.2.4 a Horizontal overhangs A change in the horizontal shading of a window facing south and west directions from 12 cm to 100 cm resulted in an internal temperature decrease of about 2% (Ali, 2013). A study done in Sao Paolo shows that the EUI (kWh/m2/year) of an office building reduces by 9% with the addition of a 1m wide, horizontal overhang as opposed to an opening with no shading device at all. Additionally, the overhang dimensions of 0m, 0.5m and 1m correspond to EUI of 183, 176 and 174 respectively. The same overhang dimensions correspond to peak cooling loads of 460, 407 and 365 respectively (de Oliveira Neves & Marques, 2017). According to Raeissi & Taheri, a building with optimum sized overhangs on the east and west walls are responsible for a reduction of 6.5% and 3.4% respectively in the cooling load on a summer day of June 21. However, if the overhangs are applied to all facades, a reduction of 12.7% is seen in the cooling loads on the same day. Similarly, research conducted in hot and humid climates shows that overhang depths of 30, 60 and 90 cm correspond to savings of 3%, 6-7% and 8-10% respectively (Wong et al, 2007).

Background

2.2.4 b WWR and shading devices A simulation of a building with a WWR of 60% shows that the east facade could be responsible for annual energy consumption of 162 kWh/m2 without horizontal shading and 150 kWh/m2 with shading; the west facade similarly corresponds to annual energy consumption of 168 kWh/m2 without horizontal shading and 154 kWh/m2 with shading. However, a significant difference was seen in the case of the south facade where a decrease of 25 kWh/m2 was seen with the addition of the shading device (Kim et al.). 2.2.4 c Other types of shading devices The addition of an egg-crate shading device to an unshaded window could account for a 5% decrease in the space temperature (Al-Tamimi & Fadzil, 2011). 2.2.4 d Density and porosity of facades

The configuration of a facade (the amount of porosity) can control the amount of flow of natural elements into the building. Elements like a double skin or a porous jaali could cut direct radiation of the sun and influence the cooling load of a space. In hot arid areas, single skin facade configurations could account for about 45% of a building’s cooling loads. On average, a double skin {additional skin at 1m offset with air outlet} for a building, in a hot & arid climate can reduce the annual cooling loads by 30% (Hamza, 2008). Research on the addition of three types of jaali perforation in a building, 30, 40 and 50%, the iteration with 50% perforation showed a better energy performance as it depended less on artificial lighting for its spaces. With the addition of 50% jaali perforation to the existing base model, a building in Lahore showed around 50.41% EUI as opposed to the initial EUI of 80.03% (Batool & Elzeyadi, 2014).

Chapter 3

Research Methodology 3.1 Area of study 3.2 A study of neighbourhoods 3.3 Local climate zone classification 3.4 Neighbourhoods selected 3.5 Hyderabad and its climate 3.6 Parameters of study 3.7 Simulations

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3.1 Area of study Hyderabad is one of the largest cities in India both in terms of its area and population. Due to the efforts of its government in promoting information technology, scientific institutions and industrial development, the city has gained national and international prominence. As a result, the city has exponentially grown outwards as compared to its metropolitan center over the past few decades. The population of Hyderabad has been on the rise since the 20th century as new opportunities were created in the city making way for migration and urban densification. The urban density in Hyderabad increased by almost 600% from 1921 to 2001 with the city center hosting a density of about 21,000 people per square km in 2001 (Venkatesh et al. 2018). Naturally, the need for housing to cater the growing population was created. A wide range of neighbourhoods were created in the city and continue to do so till date. The periphery of the city saw rapid growth as there was scope to expand without the constraint of the surrounding plots and hence large chunks of land underwent development. The case studies chosen for the paper also lie on the fringes of the city and are a testimony to this growth. All the above aspects make Hyderabad a conducive and relevant city to base the study.

Figure 4. Figure showing the various apartments identified for the study The neighbourhoods are located in the periphery of the dense neighbourhoods 34

Research Methodology

3.2 A study of neighbourhoods As the title of the paper suggests, the study revolves around residential neighbourhoods. The previous section elicits the increasing importance of the creation of households to cater the growing population. The need for energy efficiency cannot be stressed more in this century and one can say that netzero energy buildings or nearly zero energy buildings are one step towards this aspect of energy efficiency. The typology of NZEBs generally lies within office spaces, schools, institutions or any other public space but seldom a residential neighbourhood; and hence, the study tries to look at neighbourhoods. An entire neighbourhood, which is planned, designed and executed by a single body can make a greater impact while it might be cumbersome to intervene in individual units which are independently designed. A study suggests that Hyderabad has an average of 3776 households for every square kilometer (Venkatesh et al. 2018). It is also interesting to note that one out of three households is part of a society or a neighbourhood. Hence, with more localities coming up in the outskirts of the city, lesser restrictions from the surrounding context and a possibility for a tabula rasa condition unlike the city center, there is a better scope to design an energy efficient neighbourhood and hence, the paper places itself in this domain.

Figure 5. Aerial view of a neighbourhood in Hyderabad 35

3.3 Local climate zone classification As mentioned in the previous section, a number of neighbourhoods came up to facilitate the needs of a growing population. The study intends to look at three such neighbourhoods of different scales so as to get a comprehensive understanding of the overall trend in the city. Hence, different localities in the three scales, low-rise, mid-rise and high-rise categories were identified and listed down. From Figure 4 (previous page), one can notice how these neighbourhoods are spread around the fringes of the city away from the dense city center. Trends show that most of these low-rise neighbourhoods fall under LCZ 6 (open low-rise) category. Contrasting to that, most of the mid-rise neighbourhoods were identified to fall under the category of LCZ 2 (compact mid-rise). A majority of the neighbourhoods were found to have a dense built to unbuilt ratio along with a high aspect ratio (1.25-2). Furthermore, the high-rise neighbourhoods can be categorized under LCZ 4 (open high-rise). The three case studies were then chosen from the aforementioned categories. A study of these trends has shown that a lot of these neighbourhoods were constructed by a single organization under a parent name. The above classification is based on the table mentioned in Stewart & Oke, 2012.

Figure 6. Identifying trends in low-rise neighbourhoods (Source: Google Earth) Research Methodology

Figure 7. Identifying trends in mid-rise neighbourhoods (Source: Google Earth)

Figure 8. Identifying trends in high-rise neighbourhoods (Source: Google Earth) 37

3.4 Neighbourhoods selected The three neighbourhoods selected for the study are shown in Figure 9. The selection of the neighbourhoods was also affected by their accessibility due to the ongoing pandemic. NK Villa Greens is the open low-rise neighbourhood selected and is located in the Gandipet region of Hyderabad. Hallmark Tranquil is the compact midrise neighbourhood selected for the study and is located in the Manikonda region. However, Rainbow Vistas @ Rock Garden, the open high-rise neighbourhood selected for the study is situated in the Kukatpally region in the northern part of Hyderabad, slightly away from the other two case studies. Only a few units from each neighbourhood were accessible for the on-site study due to restrictions of the pandemic while all the buffer zones were accessible.

Top : Low-rise neighbourhood Center : Mid-rise neighbourhood Top : High-rise neighbourhood (Source: Google Earth)

Figure 9. Locating the neighbourhoods selected 38

Research Methodology

3.5 Hyderabad and its climate An understanding of the climate of a region is essential for any study pertaining to its built context. Hyderabad comes under the composite climate zone with the category of very hot and dry weather conditions (Climate zone 1B) as per ASHRAE standard 169-2013 Climate Zones (2) (Bhatnagar et al., 2018). The dry bulb temperature chart of Hyderabad shows that it has a comfortable temperature range of 23°C to 33°C throughout the year; however, the daytime during summers can be slightly hot; especially the afternoons of April, May and June, where it can exceed 38°C. Additionally, a few afternoons from September to November might get a little uncomfortable with temperatures ranging from 32°C to 38°C. The city has recorded an average high of 38°C and an average low of 14°C.

Figure 10. Dry bulb temperature for various months of the year Source: Climate Consultant 6.0 39

Hyderabad receives the majority of its winds from the west throughout the year. The winter winds however are from East, North-East and South-East while the monsoon winds are from West and North-West. The summer winds flow from the South, SouthWest and South-East directions. The city receives varying wind speeds due to the difference in altitudes of different regions and the presence of different local water bodies also has its own influence on the wind patterns.

Figure 11. Annual wind wheel (Source: Climate Consultant 6.0)

The months of February and May receive the highest amount of direct normal radiation and the monsoon months of August and September receive the least. In the case of diffused radiation, these monsoon months have the highest diffused radiation while it gradually decreases for the other months of the year.

Figure 12. Radiation range (Source: Climate Consultant 6.0) 40

Research Methodology

3.6 Parameters of study The literature review gave an overall idea regarding the various parameters of a building envelope which could affect energy consumption. However, after the on-site studies were conducted, only a few parameters were selected for the purpose of the research while the others were considered constants. Window-to-wall ratio (WWR) and shading devices (horizontal) were selected for the study as most of the other parameters were found to be similar in both the case studies.

Window to Wall Ratio

Envelope Properties

Fenestration

Shading Devices

Facade Treatment Figure 13. The parameters selected for the study 41

3.7 Simulation 3.7.1 An overview of the analysis The analysis in the study is based on the results of simulation of the case studies done using the software OpenStudio. A base case is modelled and simulated first and is compared with the subsequent iterations based on the parameters. Each iteration is analyzed with and without its context in order to thoroughly observe the behaviour of a unit in both conditions. The procedure is repeated for each neighbourhood and a framework is arrived at after analyzing the respective results.

OpenStudio through SketchUp

Case Study Base Model

Parameters & Simulation WWR

Shading Device Context

Iterations

Analysis

Framework

Figure 14. The method for simulation and analysis 42

Research Methodology

3.7.2 An overview of the simulation The simulation takes into consideration the climatic data of a city, in this case, Hyderabad, through its .epw file and the data about design days through the .ddy file. While modelling a unit on SketchUp, thermal zones and air-conditioned spaces have to be assigned to the unit. In order to do that, a four-meter offset is taken from the outer edge of the unit and in every space which has walls facing two different directions (for instance the corner ones), a diagonal line is drawn as shown below, creating two thermal zones in the same space. Bedroom

Hall

Bedroom

4m offset 1

Bedroom

Figure 15 a. The method for assigning thermal zones in the plan of the unit

The software gives output after simulating for an entire year. From the simulation report, the data regarding heat gain through windows is analyzed and compared as the amount of heat gain (in GJ) per unit area of the window. Furthermore, a zone-wise annual cooling load (in kWh) is also obtained and hence used for analysis. The cooling load per unit area of the zone is used for comparison. Each of these values is taken across six different levels so as to get a comprehensive idea about the whole block and its behaviour as well as to find out a gradient in the cooling loads if there are any. A construction template has been created for the case studies which include details of the building envelope like wall material, wall thickness, surface finishes and so on. However, a default activity template has been used for the simulation. Additionally, the simulation considers maximum operation hours and hence in the case of some iterations the annual consumption has shot up. Hence, the values can be considered as the upper limit while in reality, they could be less than the simulation based on the user consumption.

3.7.3 Modeling for simulation The diagram below illustrates the various components assigned while modelling a residential block for simulation in OpenStudio. The unit/the block is modelled with minimum details as every element could potentially increase the simulation time drastically, especially in case of high-rise neighbourhoods.

Block to be simulated Only the block selected for simulation will be modelled while the rest of the model would be a shading group

Shading group All the adjacent built environment will be considered as a shading group indicated by purple color; any block for which simulation results need not be applied can be modelled as a shading group to reduce the simulation time

Balcony condition The balcony was simulated as a shading surface and not a space as it is a semi-open area

Figure 15 b. Figure showing a sample block modelled for simulation 44

Research Methodology

3.7.4 Conditions for iterations As discussed earlier, the study considers two parameters for analysis. Hence, for the sake of iterations and comparison, these parameters will be changed and analyzed. WWR will be changed by doubling the width of the windows and also through a plugin in the software, while all other dimensions are left untouched. For the projection factor, it will be increased or decreased based on different conditions and simulated for comparison as shown in the adjacent diagram. Each iteration will be simulated twice; one along with its context and the other devoid of it. This way, one can infer for a local and a generic condition separately while comparing their behaviour.

WWR Increasing only the width of the windows

Horizontal shading device Increasing the projection factor

Simulating with and without the context Figure 15 c. Figure showing conditions for different iterations 45

Chapter 4 Analysis

4.1 Low-rise Neighbourhood 4.2 Mid-rise Neighbourhood 4.3 High-rise Neighbourhood

Go to Contents

4.1 Low-rise neighbourhood 4.1.1 About the neighbourhood: N K Villa Greens The neighbourhood is located in the western end of the city in Gandipet. The neighbourhood was developed in 2003; back then, the locality formed the outskirts of the city where agricultural land was still being converted for development purposes. Since then the city has grown outwards with many such neighbourhoods dotting its periphery. The neighbourhood is divided into two zones: a standard zone and free zone with 108 and 15 individual villas respectively. Apart from the two-storied villas the locality also hosts a park, an open-air amphitheater and large pathways for its residents. A salient feature of the neighbourhood is its generous built to unbuilt ratio and wide offsets allowing for vegetation to thrive. Over the course of almost two decades, the locality has formed its own micro-climate due to its percentage of vegetation cover. The locality also has a large lake in its vicinity which also affects the micro-climate of its surroundings.

4.1.2 LCZ classification The low-rise neighbourhood falls under the category of an open low-rise local climate zone (LCZ 6).

Figure 16. View of a typical free-zone residence 48

Low-rise neighbourhood

Figure 17. (Top) Lake in the vicinity in the neighbourhood (Source: Google Earth) Figure 18. Growth of the neighbourhood from 2003 to 2022 (Source: Google Earth) 49

The aspect ratio (mean height to width ratio of street canyons) of the neighbourhood is 0.25. The building surface fraction (the ratio of building plan area to total plan area) is 16% and the pervious surface fraction is 54% (the ratio of pervious plan area to total plan area). The average height of roughness elements (the geometric average of building heights and tree/plant heights) is about 8 m. These factors make it an open low-rise neighbourhood.

Building footprint: 19,692 m2 Plot Area : 1,28,936 m2

Figure 19. View of the neighbourhood with the two zones

4.1.3 User survey and on-site data collection In order to understand the user’s comfort (thermal and visual) and their energy consumption patterns, an online survey was floated among the residents of the society; about 20 responses were collected and the results were studied based on various parameters. Among the individual villas, a free-zone villa was selected so as to gather first-hand information about their energy performance, user behaviour and envelope properties. 4.1.3 a User Survey The online survey consisted of questions, both quantitative and qualitative in nature and the residents had to select the most relevant option among the ones given to them. The survey was divided into two parts; the first part included aspects about thermal and visual comfort while the second part dealt with aspects related to energy consumption. 50

Low-rise neighbourhood

Plot Area: 620 m2 Footprint: 280 m2

Figure 20. Exploded view of the typical free-zone unit 51

The questions were circulated through Google-forms and based on the following parameters: Part 1_Thermal and Visual Comfort (a) Family size (b) Location of the flat (c) Thermal comfort levels in the flat (Rating based) (d) Air movement inside the flat (Rating based) (e) Daylighting in the flat (Rating based) Part 2_Energy Consumption (f) Electricity bill for selected months (g) Number of ACs and their frequency of usage (h) Duration of operation of selected electrical appliances in the flat (i) Energy star rating of selected appliances in the flat (j) Renewable energy - provisions and possibilities (k) Optional questions regarding measures that can be taken to reduce energy consumption and in turn enhance thermal or visual comfort 4.1.3 b On-site data collection A typical free-zone villa was selected for data collection and values of different parameters were recorded for two different times of a day (12 p.m. and 6 p.m.) in the month of February with the help of instruments like the lux meter and thermal anemometer to mention a few. The villa was analyzed using the following parameters: (i) Components of the building envelope - Material and thickness of the external wall - Surface treatment (external plaster and texture) - Fenestration type and dimensions - Dimension and materiality of the shading devices (ii) Extent of the villa and treatment of offsets - Offsets on each side of the villa - Material and texture of offsets (laws, paving and so on) on each side of the villa (iii) Surface temperatures at specific points in the interior and exterior parts of the villa (iv) Lux levels in the lower floor of the villa Episode 1 of the paper involved looking at parameters like massing of the villa, the nature of recessions in the surfaces, temperatures of external surfaces and their albedo values, speed and direction of predominant winds along with their temperatures. 52

Low-rise neighbourhood

4.1.4 Inferences from the survey The data collected from the survey was tabulated into graphs and charts and the results were analyzed. The response to the surveys was very positive and a direct correlation could be seen between the observations ad the results. Most of the users were happy with their villas and were satisfied with their ventilation, natural light and thermal comfort. The inferences are listed below. 4.1.4 a Thermal comfort The survey concluded that around 76% of the users felt comfortable in their villas all year round. As the question in the survey allowed users to choose multiple options, it can be seen that about 46% of people expressed slight discomfort during peak summers and the reason can be traced back to the treatment of the large balcony on the upper floor. The balcony radiates heat to the floor below and causes the spaces to heat up. The designers tried to address this issue by introducing deep RCC pergolas on the terraces so as to reduce the sun’s direct radiation on the surface; however, this does not cut off radiation completely. Hence, one can see various user-led interventions like the introduction of potted plants and the use of climber plants to tackle this issue. A small percentage of users also felt a slight discomfort during the winters. This could stem from the fact that most users complained of the floors of their getting very cold during winters and the chilly breeze through the trees during early mornings and late evenings. The winter in Hyderabad lasts for a short time period and the users can easily adapt to the climate during winters. A very small percent of users felt stuffy inside their villas and they were identified as standard zone residents where the units are slightly dense compared to the free zone. The internal temperatures in the villas varied between 25º C and 29º C while the external surfaces (stucco plastered rough walls) showed a range of 29.5º C to 36º C, with western and south-western walls showing higher temperatures. It is interesting to note that the neighbourhood shows a 4º C dip in the temperature when compared to the outside temperatures. The vegetation can play a major role in creating such a difference and enhancing the comfort of residents given the designers plan these aspects meaningfully. 53

Figure 21. Figure showing the thermal comfort of occupants for various times of the year

4.1.4 b Air Movement Around 63% of the users felt comfortable in their villas responding positively to the amount of ventilation and emphasizing the fact that the neighbourhood is comfortable. However, around 9% of the users were unhappy with the lack of sufficient ventilation in their villas and yet again can be traced back to standard zones , where, due to obstructions like a large tree or a tight offset are responsible for limited wind flow. Furthermore, 18% of the users felt that they do not open the windows due to privacy issues, especially the windows on the upper floors and pointed out that windows were not only a source of natural light but also allowed a passer-by a peek into the space inside making the user feel uncomfortable. This could be seen when one takes a walk around the neighbourhood, the majority of the widows are curtained up and closed.

Figure 22. Figure showing the users’ preference on operability of windows and air circulation in the units

It is interesting to relate the plan of a villa to how the windows in that space operate. The survey shows that 73% of the users prefer to have their windows open on the lower floor which mostly consists of common areas like living, dining and kitchen. Additionally, 82% of the users prefer to have windows open on their first floor provided they are not using the space or the mosquito mesh is closed as the level consists mostly of bedrooms which are private in nature. Residents also mentioned that they get a good amount of wind on the lower floor and that they open the windows on the first floor to let the hot air escape from the spaces when they are not occupied. A small percentage of users are indifferent to this as they prefer the use of air conditions which require the windows to be shut. 54

Low-rise neighbourhood

Figure 23. Figure showing the users’ preference on operability of windows at different levels

4.1.4 c Daylighting One aspect that the residents of the villas are extremely satisfied with is the amount of natural daylighting they receive; this is reflected in 82% of them agreeing regarding the same. However, a small fraction of the users admitted that they use blinds/curtains to avoid heat gain through windows and due to privacy issues. A small portion of users felt that their rooms could have better daylighting and the users can be traced to villas in the standard zone which are sandwiched in-between two villas where spaces like the living and dining get dark in the afternoons or evenings due to insufficient amount of natural light. Figure 24. Figure showing the users’ opinion of daylighting in their residential units

The master plan of the neighbourhood (in the standard zone) shows the N-S oriented streets allowing the villas to be either East or West facing; the arrangement allows the villas to tap into a good amount of natural light, especially during cold winters where one can enjoy the early morning sun. The recommended amount of spatial lux requirement is 5 % and an average of the daylight factor in different rooms comes to around 3-6%. The external illuminance during the time of visit in February was observed to be around 2500 lux and the rooms had lux levels in the range of 130-150 lux while the internal corridors fell in the range of 60-75 lux. 4.1.4 d Energy Consumption

NK Villa Greens comprises of individual villas with built-up areas which range from 3500-5000 sqft. The occupancy of these villas ranges from 2-6, with around 36% of the villas inhabited by 4 people, 27% of them with 3 and 18% of them with 5 people. The residents of this gated community hail from well to do families and are financially well off. They use various electric appliances in their household ranging from televisions, geysers and dishwashers not to mention the air conditioners. Due to the large built-up area and multiple spaces in the villa which have to be air-conditioned, it can be seen that 64% of the families have 5 or more air-conditioners. Around 21% of the families have 4 ACs and 15% of them have 2-3 ACs. Air-conditioners naturally have a major share in energy consumption and they are followed by artificial lighting and other appliances. With a large carpet in the villas, one would assume a proportional amount of energy consumption but the survey tells otherwise. Hence, the way in which a neighbourhood is designed plays a major role in its energy consumption. 55

The survey gives insight into the energy consumption patterns in the neighbourhood. It can be seen that almost half the surveyees use the AC for 5-10 hours a day during summers. Around 45% of them use the AC for 3-5 hours a day depending on the weather outside. Surprisingly, 14% of them use ACs during the monsoons when it gets stuffy inside. A small portion of users admit that they use the AC for more than 15 hours a day during peak hours. The electricity bills were collected for four months of the year (to get an approximate range throughout the year) and the users had to choose a range which best depicts their monthly bill. It can be inferred that most energy is spent Figure 25. Figure showing the duration during summers with the bills reaching as high of usage of air conditioners during different seasons as Rs 5000 a month. However, winters have the least energy consumption with most of the families incurring low bills in the range of Rs. 500 to 1500. The consumption in the other months lies in-between these peaks of summer and winter. The residents of the Villa Greens showed a positive attitude towards ways in which they could reduce their energy consumption by mentioning that they turn off lights when not in use and would try to optimize their AC consumption. They also pointed out that they would look forward to the installation of renewable energy sources like solar based equipment in their neighbourhood.

Figure 26. Energy consumption of specific months of the year 56

Low-rise neighbourhood

4.1.4 e Inference The methodology of the paper is such that a selected neighbourhood is analyzed first through on-site surveys and observations and then is simulated in OpenStudio. The base case is simulated first for validating the software and then iterations are made to the best case to arrive at a result which would yield optimum results for energy efficiency. The iterations would be based on specific parameters and would feed into a framework to be developed in the end for that particular neighbourhood. However, for NK Villa Greens, as the majority of the residents feel comfortable and satisfied with their residences, the study would have to be terminated at this point and simulations would not be carried out. The neighbourhood has received a fairly positive response from its residents and the success can be pointed out to its master plan, generous green spaces and the well maintained vegetation cover. Figure 45 shows the overall rating of the neighbourhood through three parameters and user satisfaction.

Figure 27. Energy consumption of specific months of the year

4.2 Mid-rise neighbourhood 4.2.1 About the neighbourhood: Hallmark_Tranquil The neighbourhood is located in the southern end of the city in Manikonda. The locality is flanked by similar mid-rise apartment buildings which form most of the built landscape in Manikonda and its surrounding areas. It consists of four residential blocks, a clubhouse and a tennis court. The locality has the road to its East, which is its access point while the clubhouse and play area are located in the rear end. Each residential block is six storeyes high and hosts rectilinear courtyards in its center. The neighbourhood has a pathway running across its periphery and an additional play area towards the road. It has one level of underground parking and hence the residential unit start from the ground floor. The locality was developed recently in 2019 when compared to NK Villa Greens. It was developed by a group called Hallmark, which has similar mid-rise apartments in other parts of the city under the same name. This neighbourhood also has a lake in its vicinity which affects the micro-climate of the region to a small extent.

4.2.2 LCZ classification The above mid-rise neighbourhood falls under the category of a compact mid-rise zone (LCZ 2).

Figure 28. View of a typical courtyard in the neighbourhood 58

Mid-rise neighbourhood

Figure 29. Aerial view of the neighbourhood and the lake in the vicinity

Figure 30. Aerial view of the neighbourhood 59

The aspect ratio (mean height to width ratio of street canyons) of the neighbourhood is 1.6. The building surface fraction (the ratio of building plan area to total plan area) is 47% and the pervious surface fraction is 24% (the ratio of pervious plan area to total plan area). The average height of roughness elements (the geometric average of building heights and tree/plant heights) is about 17 m. These factors make it a compact low-rise neighbourhood.

Building footprint: 3930 m2 Plot Area : 8566 m2

Figure 31. View of the locality and the three units selected for study

4.2.3 User survey and on-site data collection In order to understand the energy consumption patterns and user comfort, an online survey was circulated among the residents similar to the one in the low-rise neighbourhood. A total of 20 responses were collected and analyzed based on quantitative and qualitative parameters mentioned in the previous section. The survey was divided into two parts; the first part included aspects about thermal and visual comfort while the second part dealt with aspects related to energy consumption. To gather first-hand information about the neighbourhood, an offline survey was conducted where three units located at different levels of the locality were studied and a few measurements like surface temperatures and lux levels were recorded. Apart from these parameters, some components of the building envelope like fenestrations and shading devices were also recorded. 60

Mid-rise neighbourhood

4.2.3 a Study of the units The units selected for the study are located on different levels so as to give a comprehensive understanding of the locality. The units were initially intended to be located on the same block but due to covid restrictions, a unit of the adjacent block was selected. The surface temperatures and lux levels measured in the month of February under clear sky conditions. The largest block in the plot, i.e., the A-Block, was selected as the pilot block, as it has units of varying sizes and orientation. This was followed by studying three units in the block at different floors in order to analyse changes reflected in the aforementioned values in each of the units. As done in the previous case, this study was also done at two times of the day on site (12pm and 6pm) during midFebruary, 2022. Instruments like a standard 10m measuring tape, the Lux meter, the Thermal Anemometer, the Surface Temperature gun and an Air Temperature + Relative Humidity clock were used to analyse the pilot block and individual units on the basis of similar parameters covered in the previous case study. In the neighbourhood, standard observations were made regarding depth of horizontal shading, which is found out to be 270mm. Other observations include wall thickness, plaster types and thicknesses, fenestration type and WWR (which was found to be 23%). Subsequently, U values were found out for the materials involved. Surface temperatures of each of these materials were taken at two times and compared. Shaded and unshaded surfaces were compared and window dimensions and heights from wall base were critiqued upon.

Horizontal Shading Depth: 270 mm (RCC) Thickness: 130mm (RCC) U-value: 0.31 W/m2K

External Wall Thickness: 200 mm (AAC Blocks) U-value: 0.77 W/m2K Surface Treatment: 15 mm external rough cement plaster 12 mm internal smooth cement plaster

WWR

Fenestration (UPVC Window) 6 mm clear reflective glass U-value: 1.5 W/m2K

23%

Figure 32. Components of the building envelope 61

(i) Ground floor unit_Block B1 The unit on the ground floor is located in the corner of its block and is open from three sides. The unit is surrounded by blocks of its own neighbourhood on three sides and faces an open plot (over which an apartment is to be built soon) to its west. The unit is inhabited by two people and this fact is reflected in their annual energy consumption. The various appliances used by its residents are shown in Figure 40. The monthly electricity bill here ranges from Rs 600 to Rs 1500 with peak consumption during summers. About 30% of the carpet area is air-conditioned (two bedrooms with one AC each) Figure 33. (Top) The selected unit and its neighbouring blocks Figure 34. (Below) The ground floor unit

Built-up Area: 133 m2 Carpet Area : 119 m2 Air-conditioned Area : 33.5 m2 62

Mid-rise neighbourhood

The unit is decently well lit in spite of it being on the ground floor with a tall block adjacent to it. The living and dining spaces are well lit as they face open areas while the kitchen and bedroom 2 are poorly lit as they overlook either another block or another space. The other bedrooms are moderately lit depending on the sky cover outside. However, because the unit is on the ground floor which has a lot of pedestrian activity, most of the windows are almost always covered with blinds/curtains cutting off all the natural light inside the spaces. The unit remains comfortable throughout the year as it receives mutual shading from other blocks and the floors above it and the users heavily depend on ceiling fans to keep themselves comfortable apart from the use of air conditioners. The balcony space is covered with metal mesh to prevent mosquitoes from entering inside; closed windows on top of this don’t allow natural ventilation to take place. 305

Lux Levels Surface Temperatures (clockwise)

Bedroom 2

Face B Kitchen 5

305

100

Living Bedroom 1 460

4 10

Dining Bedroom 3

Face A

Face C

993

Face D External Surface Temperatures

Internal Surface Temperatures

Face

Temperature

Space

Temperature

Face A

25.2° C, 25° C

Living

24.8° C, 24° C, 24.5° C

Face B

24.5° C

Bedroom 1

23.8° C, 24° C

Face C

26.2° C

Dining

25° C, 24.9° C, 24.8° C

Face D

25.3° C, 25° C

Bedroom 2

25.3° C, 25° C

Figure 35. The measured lux levels and surface temperatures of the unit 63

(ii) Fourth floor unit_Block A The unit on the fourth floor is located in the center of its block and opens inwards facing the courtyard. As a result, it is open from two sides, north and south. The unit is surrounded by other adjacent units on either side with no scope for opening up on its south-facing balcony as the 7m court restricts visually expansive views. The unit is inhabited by four people and, which is evidently reflected in their annual energy consumption. The various appliances used by its residents are shown in Figure 40. The monthly electricity bill here ranges from Rs 600 to Rs 1500 with an expected peak consumption occurring during summers.

Figure 36. (Top) The selected unit and its neighbouring blocks Figure 37. (Below) The fourth floor unit

Built-up Area: 117.5 m2 Carpet Area : 104.5 m2 Air-conditioned Area : 22.8 m2 64

Mid-rise neighbourhood

About 20% of the carpet area is air-conditioned (two bedrooms with one AC each) with 5-star rated ACs in both rooms. The fact that the unit is sited on the fourth floor allows for a considerable amount of daylight to penetrate into the balcony and dining spaces despite being sandwiched by other units. Similarly placed units on lower levels face a much more serious concern with added issues of daylighting. The unit witnesses lux levels in a healthy range, with internal surface temperatures marginally crossing the upper limit of Hyderabad’s comfort range of 22. The living and dining spaces experience a range of 150 lx inside the spaces and 1100 lux near openings. Most other rooms apart from bedroom 1 and bedroom 2 also have lux levels over 150-200 lx. Similarly, surface temperatures are almost of a similar range in all spaces, between 27º C-28.5º C, as a result of constant mutual shading from adjacent units. ##

Lux Levels Surface Temperatures (clockwise from North)

150

Bedroom 2 Living

273

Bedroom 1

115

Dining

Bedroom 3 510

100

260

Face B

1.6k

860

Kitchen 600

Face A

Face C

12L

Face D External Surface Temperatures

Internal Surface Temperatures

Face

Temperature

Space

Temperature

Face A

Not measured

Living

28.1° C, 28.3° C, 28.3° C

Face B

27.7° C

Bedroom 1

27.3° C, 27.4° C, 27.4° C

Face C

Not measured

Dining

28.2° C, 30° C

Face D

Not measured

Bedroom 3

27.3° C, 27.6° C

28.9° C, 29.4° C, 28.1° C Kitchen Figure 38. The measured lux levels and surface temperatures of the unit 65

(iii) Top floor unit_Block A The unit on the top floor is centrally located in the block, adjoined by units on either side. However, the unit opens up to the surrounding plot instead of the internal court, unlike the previously mentioned unit. The ample offsets between the units, and the added benefit of being situated on the topmost floor allows for ample daylight into almost all the internal spaces throughout the day. The unit is inhabited by four people. However, the use of ACs at very limited hours and the residents occupying a lesser number of rooms at a time. It allows the energy consumption of the unit to be in good range. The various appliances used by its residents are shown in Figure 40. The monthly electricity bill here ranges from Rs. 600 to Figure 39. (Top) The selected unit and its Rs. 1500 with peak consumption neighbouring blocks during summers. About 30% of the Figure 40. (Below) The top floor unit carpet area is air-conditioned (two bedrooms with one AC each)

Built-up Area: 97 m2 Carpet Area : 86 m2 Air-conditioned Area : 25.2 m2 66

Mid-rise neighbourhood

The thermal comfort is expected to not be up to the mark considering the heat gains from the rooftop. However, the considerate planning of the neighbourhood has provision for false ceiling on all the topmost floors, with significantly helps the surface temperature to be mellower than usual. The surface temperatures inside the unit were found to be in the range of 28º C-29.5º C, slightly above the comfort range limit. The lux levels inside the unit were found out to be the range of 90-500 lx, with exceptions of some corridor facing bedrooms receiving lux levels of 10-20 lx as the measurements were taken at 6:30pm. Overall, the residents seemed satisfied with the thermal and daylighting levels in the unit, and claimed that the house remains cool almost throughout the year except a few South facing rooms during peak summers.

Lux Levels Surface Temperatures (clockwise from North)

Face B

Bedroom 1 332

Kitchen

Living

137

160

Dining 34

Face A

Bedroom 2

460

Bedroom 3 360

210

Face C

5.6k

Face D External Surface Temperatures

Internal Surface Temperatures

Face

Temperature

Space

Temperature

Face A

Not measured

Living

35° C, 28.3° C, 28.3° C

Face B

28.7° C

Bedroom 1

29.1° C, 29.1° C, 29.1° C

Face C

Not measured

Dining

28.6° C, 29.1° C

Face D

Not measured

Bedroom 3

28.1° C, 28.1° C, 28.5° C

28.9° C, 29.4° C, 28.1° C Kitchen Figure 41. The measured lux levels and surface temperatures of the unit 67

4.2.4 Simulation of the neighbourhood A digital model of the neighbourhood was created on Sketchup and simulated using the software OpenStudio. The three units selected for the study were modelled and the neighbouring context was considered as a shading group so as to simulate on-site conditions closely. The monthly energy consumption was calculated and tabulated as seen in Figure 42. One can see a peak in the consumption during summers for all the three units, a gradual decrease as winter sets in and a steep increase in the initial months of the year. The simulation takes into consideration the climatic data of the city through the .epw file and the data for design days from the .ddy file.

650

Energy (kWh)

550 450 350 250 150 50

Ground

Month 1 2 3 4 5 6 7 8 9 10 11 12

Ground Unit Energy Bill 600 150 1000 250 1200 300 1500 375 1500 375 1200 300 1200 300 1000 250 1000 250 1000 250 600 150 600 150 3100

Total

7 Center

Center Unit Energy Bill 800 200 1000 250 1500 375 2000 500 2500 625 2000 500 1500 375 1000 250 1000 250 1000 250 800 200 800 200 3975

Top

Top Unit Bill Energy 500 125 1000 250 1500 375 2000 500 2000 500 1500 375 1500 375 1000 250 500 125 500 125 500 125 500 125 3250

Figure 42. Unit-wise monthly energy consumption 68

Mid-rise neighbourhood

Total 475 750 1050 1375 1500 1175 1050 750 625 625 475 475 10 325

A construction set was created for the building envelope of the neighbourhood; however, a default activity set and interior equipment were considered for the simulation. The balconies were considered as a shading group and not a thermal zone. Ideal air loads were considered only for the spaces which were air-conditioned in the units.

4.2.4 a Validation of the simulation The values for monthly energy consumption were compared with the simulated values in order to validate the results and the findings have been put up in Figure 43. As the R2 value of the graph lies between 0.7 and 0.9, the simulation can be deemed valid.

2500

y = 1.526x - 369.3 R² = 0.8848

2000

1500

1000

500

200

400

Month 1 2 3 4 5 6 7 8 9 10 11 12

600

800

Base Case 475 750 1050 1375 1500 1175 1050 750 625 625 475 475

1000

1200

Simulated Values 427.2 564.4 1231.3 1872.6 2207.5 1245.3 821.2 622.2 690.4 734.8 429.8 477.7

Figure 43. Observed values v/s Simulated values 69

1400

1600

4.2.4 b Simulation of the base case As the three units selected for the study are located in three different directions and are exposed to varying conditions of solar exposure, it would be cumbersome to analyze them and arrive at a framework in a short period of time. Hence, the simulation was carried out for the block which contained the central unit, making it the base case for the analysis. The entire block was simulated twice, once with the surrounding context and another one in isolation (devoid of context) to observe the difference in heating patterns and energy consumption. The simulation yields values pertaining to the heat gain through windows, monthly cooling loads and space-wise cooling loads to mention a few; these values would be analyzed through various iterations to arrive at an optimum set of conditions. The selected unit is a 3 BHK flat sandwiched between two units on the sides, a corridor in the front and a south-facing balcony. Only one space in the unit is air-conditioned and the same has been considered for the simulation. Only the southern face of the block is exposed to direct radiation from the sun; however, if another building would come up in the empty plot, the selected block would be mutually shaded by its neighbours. This would also mean that the amount of natural light in the flat would also decrease, especially on the lower floors.

Figure 44. The block selected for simulation and analysis 70

Mid-rise neighbourhood

Bedroom 2

Living

Bedroom1

Built-up Area

: 117.5 m2

Carpet Area

: 104.5 m2

% of air-conditioned Area : 20 WFR : 10.32%

Kitchen Dining Bedroom 3

Balcony

WWR (Gross)

: 8.5%

WWR (North)

: 0%

WWR (South)

: 24.9%

WWR (East)

: 3.8%

WWR (West)

: 5.1%

4.2.4 b (i) Heat gain through windows

Annual heat gain per unit area (GJ)

Naturally, the heat gain through windows is higher in all directions without the surrounding context. The windows facing south have a smaller difference as it faces an open plot. However, both east and west directions bring in more heat into the spaces without the context. Furthermore, the ground floor unit has the maximum heat gain through windows due to the heat reflected from the surrounding surfaces. 1.4 1.2 0

1.0

0.8

2 3

0.6

0.4 0.2

5 WC

W/O C South

Level 0 1 2 3 4 5

W/O C

West

W/O C East

South WC W/O C

W/O C

1.00 0.76 0.73 0.73 0.81 0.75

0.68 0.31 0.25 0.23 0.23 0.24

1.16 0.81 0.73 0.71 0.71 0.71

0.64 0.31 0.25 0.24 0.23 0.23

1.21 0.84 0.76 0.74 0.74 0.75

1.05 0.80 0.75 0.74 0.81 0.74

West

East

Figure 45. Annual heat gain through windows per unit area for different directions 71

4.2.4 b (ii) Annual cooling load Contrary to the previous section where the heat gain through windows decreased with the decrease in the floor level, their annual cooling load increased with an increase in the level of the unit. There is a sharp increase in the value from the ground floor to first floor, a gradual increase until fourth floor and a spike in the fifth floor due to the direct radiation from its roof. However, there is a decrease of around 200-400 units in the cooling loads due to the presence of the surrounding buildings. 5000

5000

4500

4000

Energy (kWh)

4000

3500

3000

2500

2000 2000 1500 1500 1000 1000 500 500

2 2 With context

With context

Without context

Figure 46. Annual coolings loads for different floors

The air-conditioned room is situated in the south-western corner of the room. The analysis of the cooling load per unit area of the two thermal zone shows that, in the presence of surrounding context, south and west behave similar to each other. However, without any buildings, the thermal zone facing west clearly requires more cooling than its southern counterpart due to the sun’s radiation post noon hours. 240 240

Energy (kWh)

200 200 160 160 SouthSouth WCWC 120 120

C SouthSouth W/O W/O C

C WestWest W/O W/O C

WestWest WCWC

Figure 47. Cooling load per unit area of the thermal zone 72

Mid-rise neighbourhood

Bedroom 2

Living

Bedroom1

Built-up Area

: 117.5 m2

Carpet Area

: 104.5 m2

% of air-conditioned Area : 80 WFR : 10.32%

Kitchen Dining Bedroom 3

Balcony

WWR (Gross)

: 8.5%

WWR (North)

: 0%

WWR (South)

: 24.9%

WWR (East)

: 3.8%

WWR (West)

: 5.1%

4.2.4 c Iteration 1: Increase in the number of air-conditioned spaces For the first iteration, 80% of the carpet area was air-conditioned as compared to 20% of the base case. The situation was considered keeping in mind the fact that the coming years would get hotter and would create an inevitable need for air-conditioned spaces. All the three bedrooms along with the central hall were air-conditioned. The graph below clearly indicates the jump in the cooling load with the increase in the number of ACs. On average, there is a 73% increase in the cooling loads with the surrounding buildings and a whopping 86% increase without the surrounding buildings. Similar to the base case, the cooling load increases as one moves from the ground floor to the last floor as the ceiling of the last floor is directly exposed to solar radiation.

9500

8500 8500

Energy (kWh)

7500 7500 6500 6500

W WCC20% 20%

5500

W C 80%

4500

W /O C 20%

3500

W /O C 80%

4500

W /O C 20% W /O C 80%

3500

2500

1500

500

Figure 48. Comparison of annual cooling loads in two situations 73

4.2.4 d Iteration 2: Increase in the WWR With the creation of a building in the empty plot to the unit’s south, the amount of natural light inside the units would decrease to some extent. Hence, in this iteration, the width of all the windows was increased by 50% and additional windows were introduced in the northern portion of the unit as there weren’t any due to the presence of a corridor. 750 750 650 650

Energy (kWh)

550 550

W C South W C South W C West W C West W/O C South W/O C South W/O C West W/O C West

450 450 350 350 250 250 150 150 50

50 0

Figure 49. Comparison of the cooling load per unit area of the two thermal zones

Figure 49 shows that the zone facing south behaves the same irrespective of the presence of surrounding buildings. However, the western portion requires an average of 38% more cooling load with its peak consumption on the first floor. The western portion sees a dip after first floor and a slight peak on the last floor. The southern portion gradually increases until the fourth floor and has a slight drop on the last floor. In the case of annual consumption, the peak is on the second floor and not the last floor. The graph decreases on either side of the peak.

Energy (kWh)

9500 9500 8500 8500 7500 7500 6500 6500

8.5% W C 8.5% W C 8.5% W/O 8.5% W/O C C

5500 5500 4500 4500

15.9% 15.9% WW C C 15.9% W/O 15.9% W/O C C

3500 3500 2500 2500 1500 1500 500 500

Figure 50. Comparison of annual cooling loads in two situations 74

Mid-rise neighbourhood

Bedroom 2

Living

Bedroom1

Built-up Area

: 117.5 m2

Carpet Area

: 104.5 m2

% of air-conditioned Area : 20 WFR : 18.59%

Kitchen Dining Bedroom 3

Balcony

WWR (Gross)

: 15.9%

WWR (North)

: 8.4%

WWR (South)

: 28.6%

WWR (East)

: 11.5%

WWR (West)

: 15.3%

According to a study, the walls of the intermediate floors showed higher surface temperatures than the upper floors (Wong et al, 2007). This could be the cause for the peak in the cooling load for the second and third floors. The heat gain through windows increases with the increase in WWR. In this iteration, the windows facing south and north have minimal difference with the presence or absence of surrounding buildings. The windows on the ground floor are accompanied by a large amount of heat gain as mentioned in the previous section. In the absence of a context, for the windows facing west, the lower floors bring in more heat than the upper floors; whereas, for the windows facing east, the upper floors have slightly more heat gain when compared to the lower floors.

Annual heat gain per unit area (GJ)

4.0 3.5 3.0

2.5

2.0

1.5

1.0

0.5 0.0

00 11 22 33 44 5

0.5 0.0

5 W C W/O C W C W/O C W C W/O C W C W/O C

W C W/O C W C W/O C W C W/O C W C W/O C South

South

West

East

North

Figure 51. Annual heat gain through windows per unit area for different directions 75

4.2.4 e Iteration 3: Uniform WWR In this particular iteration, a uniform WWR of 20% was introduced in all the facades with the help of the software’s plug-in and was simulated without the context to check the behaviour of each facade. 6000

Energy (kWh)

5000 4000 3000 2000 1000 0

10% WWR

20% WWR

Figure 52. Comparison of annual cooling loads in two situations

There is a clear increase in the cooling loads with the increase in WWR. There is a percentage increase of 18% to 26% on different floors while the total cooling load itself increases as one moves from the ground floor to the top floor. From figure 50, the western portion of the room is responsible for more cooling loads than its southern counterpart; this holds true for both cases.

Energy (kWh)

The heat gain per unit area says shows that, except for the northern facade, all the three facades bring a comparable amount of heat into the spaces; with the increase in WWR, the south brings in slightly more heat when compared to the west and the east. 300

300

250

200

150

WWR West 10%10% WWR West

100

WWR South 20%20% WWR South WWR West 20%20% WWR West

WWR South 10%10% WWR South

Figure 53. Comparison of the cooling load per unit area of the two thermal zones 76

Mid-rise neighbourhood

Bedroom 2

Living

Bedroom1

Built-up Area

: 117.5 m2

Carpet Area

: 104.5 m2

% of air-conditioned Area : 20 WFR : -

Kitchen Dining Bedroom 3

Balcony

WWR (Gross)

: 20%

WWR (North)

: 20%

WWR (South)

: 20%

WWR (East)

: 20%

WWR (West)

: 20%

300

250

300

200 150

100

150

3 0

150

200

100

200

250

50 30 2

0 4 1

1.4 1.2 1.4 0

1.0 1.2

0.8 1.0

0.6 0.8

0 1 2

0.4 0.6

0.2 0.4

10% WWR South 10% WWR South10% WWR South 10% WWR South 5 0.0 0.2 10% WWR West10% WWR West10% WWR West10% WWR West South West East North 20% WWR 20%South WWR South 20%South WWR 20% WWR South 10% WWR South 10%South WWR 10% WWR 10%South WWR South 0.0 20% WWR 20%West WWR West 20%West WWR 20% WWR West 10% South WWR West 10%West WWR 10% WWR 10%West WWR West West East North 20% WWR South20% WWR South20% WWR South 20% WWR South South West East North 20% WWR West20% WWR West20% WWR West 20% WWR West 10% 20% 10% 20% 10% 20% 10% 20%

100 1

Annual heat gain per unit area (GJ)

The ground floor seems to have a slightly larger heat gain as compared to the other floors due to the heat being reflected from surfaces (as the material of the surrounding surfaces have not been assigned in the model and the software assumes a default material). With the increase in WWR, in the western and eastern facades, the floors behave closely while they show slight deflection in the southern facade.

Floor 0 0 1.05 41 30 52 41 1 0.80 2 30 53 2

0.75 41 0.74 3 52

1.47 52 1.03

1.16 3 0.81

1.20 4 0.98

1.21 5 0.84

1.15

0.85

1.00

0.40

0.95 4 3 1.15

0.73 50.714

0.94 5 0.92

0.76

0.97

0.34

0.74

0.84

0.32

0.81

1.05

0.71

0.92

0.74

0.95

0.32

0.74

1.01

0.71

0.92

0.75

0.95

0.33

Figure 54. Annual heat gain through windows per unit area for different directions 77

4.2.4 f Iteration 4: Increasing the projection factor The southern face of the block is the only portion exposed directly to the sun. Hence, in this iteration, the projection factor of the windows facing south was increased to 0.5 while in the other facade, it remained 0.23. This case was simulated with the buildings around it and hence the difference in the projection factors. Additionally, the massing of the blocks in the neighbourhood is done in such a way that every unit has a small buffer (about 2m) on its lateral sides so as to ensure the flow of natural light and ventilation. All these buffer spaces are covered with a canopy made of polycarbonate sheet and remain shaded throughout the day. Similar to the previous case, the projection factor of all the southern windows was increased to 0.83 and was simulated along with its context. The projection factors were chosen due to the reasons mentioned in the previous chapter. Any projection factor more than 0.83 (here) would result in large overhangs which would be difficult to construct. In this paper, only horizontal shading devices have been analyzed. (i) Case 1_Increasing the projection factor to 0.5 only in the southern facade (ii) Case 2_Increasing the projection factor to 0.83 only in the southern facade Annual heat gain per unit area (GJ)

1.2 1.2

11 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0 0

0 0

1 1

2 2 0.23 0.23

3 3 0.5 0.5

4 4

0.83 0.83

Figure 55. Comparison of heat gain per unit area of the windows facing South WEST FLOOR 0 1 2 3 4 5

0.23 0.68 0.31 0.25 0.23 0.23 0.24

0.5 0.68 0.31 0.25 0.23 0.23 0.23

EAST 0.83 0.68 0.31 0.25 0.23 0.23 0.23

FLOOR 0 1 2 3 4 5

0.23 0.64 0.31 0.25 0.24 0.23 0.23

0.5 0.64 0.31 0.25 0.24 0.23 0.23

0.83 0.64 0.31 0.25 0.24 0.23 0.23

Figure 56. Comparison of heat gain per unit area of the windows facing west and east 78

Mid-rise neighbourhood

Bedroom 2

Living

Bedroom1

Kitchen

Existing Projection Factor

Dining Bedroom 3

: 0.23

(In all directions) Proposed Projection Factor 1 : 0.5

Balcony

Proposed Projection Factor 2 : 0.83

As seen in Figure 55, the heat gain per unit area through windows in the southern facade decreases gradually with the increase in the projection factor. Additionally, the ground floor has the highest heat gain and all the other floors have similar amount of heat gain except for fourth floor which has a slight peak. However, the heat gain through windows facing east and west hardly have a difference due to the presence of surrounding buildings. Similarly, a slight decrease is seen in the annual cooling loads as the projection factor increases. In the case of 0.5 being the projection factor, a decrease of 1% to 2.6% can be seen and the percentage decrease also decreases as the floor level increases. Similar to the previous case, the projection factor of 0.83 yields a decrease ranging from 1.6% to 3.7%. Contrary to the heat gains which decrease as the floor level increases, the cooling load increases as the level increases with cooling load of the fifth floor increasing by 500% when compared to ground floor. The percentage decrease might feel insignificant but, they are due to a change in the projection factor of only two windows in the south facade. A significant difference could be achieved in the energy consumption of an entire block if small details like these are designed with care and scientific attention. Unit-wise annual cooling load (kWh) FLOOR 0 1 2 3 4 5

0.23 747.2 3455.6 4050.0 4150.0 4150.0 4650.0

0.5 727.8 3408.3 4002.8 4105.6 4105.6 4605.6

0.83 719.4 3380.6 3972.2 4072.2 4075.0 4577.8

Percentage decrease in cooling load FLOOR 0 1 2 3 4 5

0.23 -

0.5 2.6 1.4 1.2 1.1 1.1 1.0

0.83 3.7 2.2 1.9 1.9 1.8 1.6

Figure 57. Comparison of unit-wise annual cooling loads (kWh) for different iterations and their percentage decrease 79

4.2.4 g Iteration 6: Uniformly increasing the projection factor The following iterations were simulated without the surrounding buildings and the increase in projection factor was applied to windows on all facades. However, the large window in the balcony and its projection were retained in their original proportions. As the northern facade had no windows in the base case, these iterations which followed also don’t consider them. The thickness of the horizontal shading device has been considered as a constant for the sake of the study. The iterations of the study were simulated such that each case would have two conditions (with and without a context) which would be compared for the making of a framework. However, in the case of the shading devices, a reverse method was adopted - as can be seen through this iteration and the previous one. (i) Case 1_Increasing the projection factor uniformly to 0.5 (ii) Case 2_Increasing the projection factor uniformly to 0.83

Annual heat gain per unit area (GJ)

1.2 1.2 11 West| 0.23 | 0.23 West

0.8 0.8

West| 0.50 | 0.50 West West| 0.83 | 0.83 West

0.6 0.6

East| 0.23 | 0.23 East 0.4 0.4

East| 0.50 | 0.50 East East| 0.83 | 0.83 East

0.2 0.2 00

Figure 58. Comparison of heat gain per unit area of the windows facing west and east

FLOOR 0 1 2 3 4 5

0.23 1.05 0.80 0.75 0.74 0.81 0.74

SOUTH 0.5 0.97 0.71 0.67 0.66 0.73 0.66

0.83 0.93 0.67 0.62 0.62 0.68 0.62

FLOOR 0 1 2 3 4 5

0.23 -

SOUTH 0.5 8 10 11 11 10 11

Figure 59. Comparison of heat gain per unit area of the windows facing south 80

Mid-rise neighbourhood

0.83 12 16 17 17 16 17

Bedroom 2

Living

Bedroom1

Kitchen

Existing Projection Factor

Dining Bedroom 3

: 0.23

(In all directions) Proposed Projection Factor 1 : 0.5

Balcony

Proposed Projection Factor 2 : 0.83

Figure 58 shows that the windows facing west bring in more heat when compared to the ones facing east when simulated without adjacent buildings. The heat gain through windows facing east and west decreases as one moves from the ground floor to top floor. The projection factor of 0.5 decreases the heat gain per unit area upto 11% while its 0.83 counterpart significantly decreases the heat gain by 17% in the windows facing the south direction. Similar to the previous iteration, the cooling load increases as one moves from the ground floor to top floor in all the iterations with the top floor consuming around five times to that of ground floor. Additionally, the projection factors 0.5 and 0.83 bring around 1% and 2% decrease in the cooling loads. When one looks at the month-wise energy consumption, the iterations follow the base case where an annual peak is reached in May and the consumption decreases on its either sides with a slight peak in October. The fact that horizontal shading devices of the same dimension were used in all facades irrespective of them being located in a shaded buffer space triggered the current iteration. A conscious choice of shading devices can save a lot of resources from getting wasted.

FLOOR 0 1 2 3 4 5

0.23 900.0 3802.8 4391.7 4469.4 4447.2 4919.4

0.5 872.2 3741.7 4327.8 4405.6 4386.1 4863.9

0.83 858.3 3708.3 4294.4 4372.2 4352.8 4833.3

FLOOR 0 1 2 3 4 5

0.23 -

0.5 3 2 1 1 1 1

0.83 5 2 2 2 2 2

Figure 60. Comparison of unit-wise annual cooling loads (kWh) for different iterations and their percentage decrease 81

4.3 High-rise neighbourhood 4.3.1 About the neighbourhood: Rainbow Vistas_Rock Garden The neighbourhood is located in the north end of the city in Kukkatpally. It is formed around a large open garden bound by twenty storey high blocks thirteen in number. The neighbourhood consists of residential units of various sizes ranging from 2-BHK units to 4-BHK units apart from amenities like a restaurant, swimming pool, play area and so on. The vehicle parking for the entire neighbourhood is accommodated in three basement levels and hence the residential units start from the ground level. The neighbourhood is flanked by two large water bodies on either side which exert their influence on its micro-climate and wind patterns. Rainbow vistas is part of a larger township consisting of similar building complexes and amenities. The entire complex was completed in the year 2017. The neighbourhood was awarded the Indian Green Building Council (IGBC) Green Homes Pre-Certification and a LEED GOLD in 2012.

4.3.2 LCZ classification The above high-rise neighbourhood falls under the category of an open high-rise zone (LCZ 4).

Figure 61. View of a typical atrium space between two blocks in the neighbourhood 82

High-rise neighbourhood

Figure 62. The neighbourhood and the lakes in the vicinity

Figure 63. Aerial view of the neighbourhood 83

The neighbourhood has an aspect ratio of 2.1. The building surface fraction (ratio of building plan area to total plan area) is about 23% and the pervious surface fraction is about 40%. The height of roughness elements is 60 m. Due to the above mentioned parameters, Rainbow Vistas can be categorized as an open high-rise neighbourhood.

Building footprint: 22,500 m2 Plot Area : 97,567 m2

Figure 64. A view of the neighbourhood and the arrangement of residential blocks around a large central space (Source: Ritwik Behuria, 2022)

4.3.3 User survey and on-site data collection In order to understand the user’s comfort (thermal and visual) and energy consumption patterns, an on-line survey was floated among the residents of the society; about 20 responses were collected and the results were studied based on various parameters. Among the 13 blocks of the society, 3 units located at different levels were selected so as to gather first-hand information about their energy performance, user behaviour and envelope properties. 4.3.3 a User Survey The online survey consisted of questions, both quantitative and qualitative in nature and the residents had to select the most relevant option among the ones given to them. The survey was divided into two parts; the first part included aspects about thermal and visual comfort while the second part dealt with aspects related to energy consumption. 84

High-rise neighbourhood

The questions were based on the following parameters: Part 1_Thermal and Visual Comfort (a) Family size (b) Location of the flat (c) Thermal comfort levels in the flat (Rating based) (d) Air movement inside the flat (Rating based) (e) Daylighting in the flat (Rating based) Part 2_Energy Consumption (f) Electricity bill in selected months (g) Number of ACs and their frequency of usage (h) Duration of operation of selected electrical appliances in the flat (i) Energy star rating of selected appliances in the flat (j) Renewable energy - provisions and possibilities (k) Optional questions regarding measures that can be taken to reduce energy consumption and in turn enhance thermal or visual comfort 4.3.3 b Inferences from the survey The data collected from the survey were tabulated into graphs and charts and the results were analyzed. The inferences are listed below. (i) Thermal Comfort The survey infers that most of the users (about 75%) feel comfortable throughout the year. This could be a result of the fact that most of the residential units in the neighbourhood are bound by other units on either side and mutually shade each other. Around 25% of the users had no concerns regarding their thermal comfort; these users were found to be living in such strategically located units. This aspect was further proved during the visit to the neighbourhood. Most of the blocks mutually shaded each other; only the corner units and the units on the topmost level were directly exposed to the sun resulting in about 40% of the users expressing discomfort during summers.

Figure 65. Figure showing the thermal comfort of occupants for various times of the year

Around 15% of the users feel uncomfortable due to humidity during the monsoons. This could stem from the fact that the arrangement blocks doesn’t let winds into the buildings and because users close their windows, the air inside the units tends to get humid. The surveys also revealed that around 30% of the users felt a little cold during winters; upon interacting with the residents, it was found out that the kind of flooring used inside the flats tends to get cold during winters and hence the discomfort. (ii) Air Movement The survey shows a variety of user patterns regarding the operability of windows. Users’ were allowed to choose more than one option and hence a range of results can be seen. About 38% of the users prefer to open their windows as they receive good amount of winds; these users can be traced back to units which are located in the corners, in the windward area or in the upper floors where there is a good flow of winds. It is also seen that around 25% of them prefer to have their windows open, but feel that they do not receive ample amount of winds and 29% of them have their windows shut due to the same reason. The occupants of these units can be identified as those in the wind-shadow region and the units in the center of each floor sandwiched by other units on either sides especially in the lower floors. It is interesting to note that around 29% of the users prefer to shut their windows due to privacy issues; a walk around the neighbourhoods also reveals that most of the units in the lower floors have their windows shut and all the windows facing the corridors are almost always shut as the ground floor and corridors are active spaces with people moving around and this could hinder their privacy. A small percentage of users prefer to have their windows closed as they use their air conditioners; this is clearly a result of the rising temperatures every year and has a direct impact on the user behaviour and their tendency to install and use such mechanisms to keep their spaces cooler than the ambient.

Figure 66. Figure showing the users’ preference on operability of windows and air circulation in the units 86

High-rise neighbourhood

(iii) Daylighting A majority of the users, around 62% of them feel that only a few rooms in their flat receive a good amount of natural light while the other rooms are poorly lit. This could be a clear reflection of the fact that the residential blocks are oriented in the N-S direction and they mutually shade each other resulting in darker interiors; the issue is also further worsened by the fact that all units except for the corner ones are sandwiched between other units. The lack of enough natural light could also be because of the minimal offset between each unit on the floor and the aspect ratio of the block itself. Figure 67. Figure showing the users’ About 33% of the users are not satisfied with opinion of daylighting in their residential the amount of natural light they receive in their units flats while 17% of them admit their inevitable dependence on artificial lighting. On the other hand, the survey shows that 29% of the respondents are happy with the amount of daylight they receive but are not able to enjoy it as they use curtains for privacy. Additionally, about 20% of them receive an ample amount of daylight but are concerned about the heat gain which comes with it. These results prompt designers to re-think the kind of fenestrations installed in the units where one needs natural light and privacy and has to avoid heat gain at the same time.

(iii) Energy Consumption Rainbow Vistas consists of a variety of units which range from 1250 to 2795 square feet of built up area. The number of occupants in each unit range from 2 to 6 apart from the floating population. The survey shows a direct relation between the size and occupancy of a unit and its energy consumption. The pandemic lead to a spike in the energy consumption of residences across the globe and has altered patterns of residence occupation with work from home being the new normal. The energy consumption of each unit was noted for strategic four months (covering different seasons) of the year through their electricity bill as the electricity meters (which would have provided a more comprehensive picture) were not accessible.

Similar to the previous neighbourhoods, the energy consumption has a peak during summers with around 50% of them expending more than Rs.2,500 per month. This was confirmed through the survey, where around 42% of the occupants use air conditioners for 5 to 9 hours everyday. Surprisingly, a small percentage of residents also use ACs for 3 to 5 hours during monsoons and winters. A majority of the residents incur electricity bills of around Rs.500 to Rs.1500 throughout the year except for summers. However, during summers, a small number of residents incur bills in the range of Rs.4500 or more; this number is a clear indication of the inevitable Figure 68. Figure showing the duration of dependence on artificial cooling in the years to usage of air conditioners during different seasons come. Another aspect that was revealed during the surveys was that the residents were willing to save energy by using appliances only when required and to use energy-efficient ones. They were unhappy with the fact that the neighbourhood lacked any kind of renewable energy provisions and were looking forward to their installation in the future.

Figure 69. Figure showing the users’ preference on operability of windows and air circulation in the units 88

High-rise neighbourhood

4.3.3 c Study of the units The units selected for the study are located on different levels so as to give a comprehensive understanding of the locality. The units were initially intended to be located on the same block but due to covid restrictions, a unit of the adjacent block was selected. The surface temperatures and lux levels measured in the month of February under clear sky conditions. All blocks are placed linearly in two opposite rows of the plot, in a North-South orientation. A standard sandwiched block of the plot, the M-Block, was selected as the pilot block, which consists of 10 units of East-West orientation. This was followed by studying three units in the block at different floors in order to analyse changes reflected in the aforementioned values in each of the units. As done in the previous case, this study was also done at two times of the day on site (12pm and 6pm) during mid-February, 2022. Instruments like a standard 10m measuring tape, the Lux meter, the Thermal Anemometer, the Surface Temperature gun and an Air Temperature + Relative Humidity clock were used to analyse the pilot block and individual units on the basis of similar parameters covered in the previous case study. On-site, standard observations were made regarding depth of horizontal shading, which is found out to be 270mm. Other observations include wall thickness, plaster types and thicknesses, fenestration type and WWR (which was found to be 23%). Subsequently, U values were found out for the materials involved. Surface temperatures of each of these materials were taken at two times and compared. Shaded and unshaded surfaces were compared and window dimensions and heights from wall base were critiqued upon.

Horizontal Shading Depth: 600 mm (RCC) Thickness: 130mm (RCC) U-value: 0.31 W/m2K

External Wall Thickness: 200 mm (AAC Blocks) U-value: 0.77 W/m2K Surface Treatment: 15 mm external rough cement plaster 12 mm internal smooth cement plaster

Fenestration (UPVC Window) 6 mm clear reflective glass U-value: 1.5 W/m2K Figure 70. Components of the building envelope 89

(i) Third floor unit_Block M The unit on the sixth floor is centrally located in the block, adjoined by units on either side and offset slightly inwards in the block plan. The unit opens up to the open court. However, the location of the unit, both in terms of floorplate and level has caused major discomfort in the daylighting aspect throughout the year, according to the residents. Similarly, it creates thermal discomfort as well during winters as a consequence of the same. The unit feels too cold due to a deficit of the crucial winter sun, due to the lack of heat gain from any other source and the provision of vitrified tiles. Figure 71. (Top) The selected unit and its neighbouring blocks Figure 72. (Below) The third floor unit

Built-up Area: 104.42 m2 Carpet Area : 95 m2 Air-conditioned Area : 76.6 m2 90

High-rise neighbourhood

A restricted use of ACs due to the comfortable thermal range, added with lower number of occupancy hours as a result of the residents being working adults helps keep the electricity consumption in check. The surface temperatures inside the unit were found to be well within the comfort range, between 25º C-26º C without the need for mechanical cooling. Additionally, the spaces felt cooler as a result of the flooring and ceiling being devoid of any heat gain. However, the lux levels inside the unit were found to be alarmingly low as seen in the figure below. The residents use artificial lighting even during daytime and admit that it is a constant requirement.

Lux Levels Surface Temperatures (clockwise from North)

1 3

Face B

Living

Kitchen

Bedroom 1

1 10

Face A

Dining Bedroom 2

Face C 11

140

Bedroom 3

300

230

Face D External Surface Temperatures

Internal Surface Temperatures

Face

Temperature

Space

Temperature

Face A

Not measured

Living

26.7° C, 26.7° C, 26.6° C

Face B

25.9° C

Bedroom 1

26.3° C, 25.8° C, 26.3° C

Face C

Not measured

Dining

26.6° C, 26.7° C

Face D

Not measured

Bedroom 3

25.6° C, 26.2° C

Kitchen

26.7° C, 26° C, 26.5° C

Figure 73. The measured lux levels and surface temperatures of the unit 91

4.3.4 Simulation of the neighbourhood Similar to the previous neighbourhood, a digital model of the neighbourhood was created on Sketchup and simulated using OpenStudio. The three units selected for the study were modeled and the neighbouring context was considered as a shading group so as to simulate on-site conditions closely. However, one unit (the unit on the third floor with a west-facing balcony) was selected among the three units and its block was simulated for analysis so as to arrive at a framework. The monthly energy consumption was calculated and tabulated as seen in Figure 45. One can clearly see a peak in the consumption during summers for all the three units, a gradual decrease as winter sets in. As the percentage of air-conditioned area varies from each flat, it affects their annual energy consumption accordingly. 1600 1400

Energy (kWh)

1200 1000 800 600 400 200 0

4 Ground

Month 1 2 3 4 5 6 7 8 9 10 11 12

Ground Unit Bill Energy 1000 250 1500 375 2000 500 4500 1125 4500 1125 4000 1000 2000 500 2000 500 1500 375 1500 375 1500 375 1000 250 6750

7 Center

Center Unit Bill Energy 1800 450 2000 500 5000 1250 6000 1500 6000 1500 5000 1250 2000 500 2000 500 2000 500 2000 500 2000 500 1800 450 9400

Top Top Unit Bill Energy 1100 275 1500 375 1500 375 2000 500 2000 500 1700 425 1700 425 1700 425 1500 375 1500 375 1100 275 1100 275 4600

Figure 74. Unit-wise monthly energy consumption 92

High-rise neighbourhood

Total 975 1250 2125 3125 3125 2675 1425 1425 1250 1250 1150 975 20 750

Similar to the previous set of simulations, a construction set was created for the building envelope while the default template was used for assigning the activity set and interior lighting and equipment. The balcony and utility areas were modelled as shading groups and not as spaces. Ideal air loads were considered only for the spaces which were airconditioned in the units. As the simulation is already validated, the study would proceed with simulating the base case. Iterations would be proposed for different parameters and each iteration would be simulated with and without its surrounding buildings. A point to note here is that the block considered for simulation is a vertical stack of the highlighted unit shown in Figure 72 and is a small part of the larger block selected for the on-site study. The selected unit is a 3 BHK flat sandwiched between two units on the sides, a corridor to its front and faces another block of the neighbourhood to its west. A total of four spaces in the unit are air-conditioned including the hall and hence it has the highest energy consumption of the three flats selected. Similar to the previous neighbourhood, all the units in the block except for the corner ones have a small buffer of around three meters on either sides. However, these spaces are not covered with a canopy and are exposed to weather conditions. Due to the aspect ratio of these buffers and the N-S orientation of the blocks, most of them remain dark throughout the day and become pigeon infested dingy spaces even though they were intended to facilitated proper light and ventilation into the units.

Figure 75. The block selected for simulation and analysis (Source: Ritwik Behuria, 2022) 93

4.3.4 a Simulation of the base case The typical unit selected for simulation is the one on third floor of the M-block in Rainbow Vistas. The unit and all the units present vertically above or below it were modelled and simulated together.

4.3.4 a (i) Heat gain through windows Figure 72 indicated that the heat gain through windows naturally increased in the absence of the surrounding buildings. Additionally, west has the maximum heat gain followed by the south and north. This could be attributed to the western face of the unit which is exposed to sun’s direct radiation. Yet again, ground floor seems to bring in a significant amount of heat when compared to the other floors and is competing with the last floor which also has maximum heat gain from these directions. Except for 19th, 15th and the ground floor, the other floors seem to behave similarly in case of the windows facing west. North seems to have minimum deviation without the presence of other buildings. Moreover, for south facing windows, heat gain changes by a small factor for the intermediate floor levels.

Annual heat gain per unit area (GJ)

1.2

1.1 1.1 1.0 1.0

G G

0.9 0.9 0.8 0.8

0.5

15 15 19

0.6

0.5

W/O C

West

W/O C South

W/O C

South

West

W/O C

North

W/O C

North

South

North

Level

W/O C

0.78

1.19

0.80

1.08

0.77

0.86

0.64

1.04

0.65

0.93

0.64

0.81

0.64

1.03

0.65

0.93

0.64

0.74

0.64

1.02

0.65

0.92

0.64

0.74

0.71

1.01

0.66

0.91

0.63

0.73

1.13

1.15

0.99

1.06

0.81

0.79

Figure 76. Annual heat gain through windows per unit area for different directions 94

11 11

0.7 0.7 0.6

High-rise neighbourhood

Bedroom 2

Living

Kitchen

Built-up Area

: 104.5 m2

Carpet Area

: 94.5 m2

% of air-conditioned Area : 80 WFR : 19%

Dining Bedroom 3

Bedroom1 Balcony

WWR (Gross)

: 11.8%

WWR (North)

: 12.2%

WWR (South)

: 6.1%

WWR (East)

: 0%

WWR (West)

: 29.1%

4.3.4 a (ii) Unit-wise cooling loads The graph for the annual cooling loads shows an interesting pattern. The cooling load for intermediate floors has very less deviation while the ground and last floors have a drastic dip. A similar pattern can be seen in the heat gain per unit area of windows facing different directions. 14000

Energy (kWh)

12000 10000 8000 6000 4000 2000

With Context

Without Context

Figure 46. Annual coolings loads for different floors West

South

North

East

Level

W/O C

37.5

60.7

32.4

49.0

43.1

52.3

25.7

42.7

137.0

173.6

135.4

170.6

153.0

167.0

124.3

153.5

136.2

170.9

135.2

167.8

151.5

164.9

123.3

150.5

135.6

167.4

133.5

164.2

149.6

161.9

121.9

146.7

140.4

165.0

134.6

161.7

149.6

159.5

124.3

144.2

66.4

67.7

54.3

55.9

60.4

62.3

48.6

50.0

Figure 77. Cooling load per unit area of the thermal zones for different directions 95

4.3.4 b Iteration 1: Increase in the WWR 14000

Energy (kWh)

12000 10000

6000

4000

14000 12000 10000

8000

4000 2000

Chart Title

8000 6000

2000 0

12% | W C

12% | W/O C

17% | W C

17% | W/O C

Figure 78. Comparison of annual cooling loads 180

Energy (kWh)

160 140 120 100 80 60 40 20 0

Chart Title

225 200 175

Energy (kWh)

225 200 175 150 125 100 75 50 25 0

150 125 100 75 50 25 0

West

South

North

East

Figure 79. Comparison of the cooling load per unit area of thermal zones in different directions Top: With context | Bottom: Without context 96

High-rise neighbourhood

The WWR of the unit was increased to 17% by increasing the width of 12each W C window by 50%. 12 W/O C Additionally, windows 17 W C were introduced in the 17 W/O C east facade to observe its behaviour without the presence of its surrounding buildings; however, the large window in the balcony was left untouched. On an average, there is a 14% increase in the annual cooling loads with context and it is 15% without West South context due to the increase North in WWR. Moreover, the East trend within the floors has slightly but the overall they behave in the same way. In the presence of surrounding buildings, the spaces facing east and west take up more cooling load followed by north and West south. However, in the case South of isolated simulation, North the spaces facing north East or south behave similarly while east and west require an increased cooling load. 19 Yet again, the rooms in the ground and top floor take us lesser cooling load compared to the other floors.

Bedroom 2

Living

Kitchen

Built-up Area

: 104.5 m2

Carpet Area

: 94.5 m2

% of air-conditioned Area : 80 WFR : 28%

Dining Bedroom 3

Bedroom1 Balcony

WWR (Gross)

: 17%

WWR (North)

: 18.3%

WWR (South)

: 9.2%

WWR (East)

: 8.5%

WWR (West)

: 33.6%

The increase in WWR naturally increases the heat gain through windows. Similar to the base case, windows facing west and east bring in more heat followed by south and north. In all the directions, the intermediate floors (3,6,11,15) show similar behaviour while the top floor and ground floor show peak values. It is interesting to note that in sixth and fifteenth floors, the windows facing west bring in more heat as compared to the other floors. 1.4 Annual heat gain per unit area (GJ)

1.3

1.4

1.2

1.3

1.1

1.2

1.0

1.1

0.9 0.8 0.7 0.6

1.0 0.9 0.8

0.7 0.6

C CC WW/O C W/O CW CW C W/O W/O West West

WWCC

South South

West

W/O C W/O C

East East

W/O WW C C W/O C C North North

South

East

North

Floor

W/O C

0.77

1.17

0.78

1.04

0.76

1.09

0.76

0.84

0.63

1.02

0.64

0.90

0.64

0.98

0.63

0.72

0.83

1.33

0.64

0.89

0.64

0.97

0.63

0.72

0.63

1.00

0.64

0.89

0.64

0.96

0.63

0.71

0.93

1.33

0.64

0.88

0.64

0.95

0.62

0.71

1.11

1.13

0.96

1.02

1.01

1.06

0.78

0.83

Figure 80. Annual heat gain through windows per unit area for different directions 97

4.3.4 c Iteration 2: Uniform change in the WWR

Energy (kWh)

For the next iteration, the WWR of all the facades was uniformly increased to 20% using a plug-in from the software and was simulated without the context to observe the behaviour of each facade. Each case here has been simulated without the context. 14000

14000

12000

10000

8000

6000

4000

2000

2000 0

10 10% WWR 17 17% WWR 20 20% WWR

611

11 15

15 19 19

Figure 81. Comparison of annual cooling loads

The iteration with 20% WWR shows a pattern similar to the base case with a slight increase of around 9% to 16% in different floors. The ground floor has the least consumption while the intermediate floors show a slight gradual decrease from third floor to fifteenth floor. The table below shows that the zone facing west requires the maximum cooling load while the ones facing north require the least and is followed by the south facing zones. With the increase in WWR, the spaces facing west experience around an 18% increase in cooling loads with an exception for the ground floor and the last floor. Similar to the overall cooling load, the zone-wise cooling loads per unit area show a gradual decrease from third floor to fifteenth floor. The heat gain per unit area of the windows on western facade have a slight increase compared to the base case. West

South

North

East

Level

12%

20%

12%

20%

12%

20%

12%

20%

73.8

106.2

49.0

73.4

52.3

68.0

42.7

92.8

191.7

227.7

170.6

200.2

167.0

185.7

153.5

218.5

188.4

221.7

167.8

196.9

164.9

183.6

150.5

215.1

184.3

217.1

164.2

192.8

161.9

180.4

146.7

210.8

138.0

204.4

161.7

190.0

159.5

177.8

144.2

207.8

130.4

105.9

55.9

78.4

62.3

78.2

50.0

97.1

Figure 82. Comparison of the cooling load per unit area of thermal zones in different directions 98

High-rise neighbourhood

Bedroom 2

Living

Kitchen

Built-up Area

: 104.5 m2

Carpet Area

: 94.5 m2

% of air-conditioned Area : 80 WFR : 29.6%

Dining Bedroom 3

Bedroom1 Balcony

WWR (Gross)

: 20%

WWR (North)

: 20%

WWR (South)

: 20%

WWR (East)

: 20%

WWR (West)

: 20%

Annual heat gain per unit area (GJ)

The windows facing east bring in more heat when compared to the ones facing west in this iteration. This could be attributed to the heat gain in the early hours of a day. The windows facing north, however, have the least deviation from the base case. The windows facing south, the heat gain increases from the ground floor to the sixth floor, reaches a minimum there and increase again in the upper floors. 1.40 1.20 1.00 0.80 0.60 0.40 0.20

1.4 1.2 1

0.8

0.6

0.4

0.2

Floor

G Floor

3G 6 6 3 1111 6 1515 11 1919 15

19 South EastSouth North West West West West West South South West West South EastWest East West South South EastEast North North South South EastEast North North Eas 0.00 West South East North Floor Floor 12% Floor Floor20%20% 12%12% Floor 12% Floor 12% 20%20% 12% 20% 12% Floor 20% 12% Floor 12% 20% 12% 20% 12% 12% 20% 12% 20% 12% 20% 12% 20% 20% 12% 20% 12% 20% 12% 20% 12% 12% 20% 12% 20% 12% 20% 12% 20% 20% 12% 20% 12% 20%20% 12% 20% 12% 20% 12% 012% 0.2

G G

1.161.16 GWest G 1.211.21 1.161.16 G1.08 G1.08 1.211.21 1.16 0.71 1.16 0.71 G1.08 G1.08 1.21 0.00 1.21 0.00 1.16 0.71 1.16 0.71 1.08 1.27 1.08 1.27 1.21 0.00 1.21 0.00 0.71 0.86 0.71 0.86 1.08 1.27 1.08 1.27 0.00 0.87 0.00 0.87 0.71 0.86 0.71 0.86 1.271.27 0.00 0.87 0.00 0.87 0.8 South East North

3 3

1.041.04 3 3 1.091.09 1.041.04 30.93 30.93 1.091.09 1.04 1.07 1.04 1.07 30.93 30.93 1.09 0.00 1.09 0.00 1.04 1.07 1.04 1.07 0.93 1.15 0.93 1.15 1.09 0.00 1.09 0.00 1.07 0.81 1.07 0.81 0.93 1.15 0.93 1.15 0.00 0.76 0.00 0.76 1.07 0.81 1.07 0.81 1.151.15 0.00 0.76 0.00 0.76 0.8

6 6

1.031.03 6 6 1.081.08 1.031.03 60.93 60.93 1.08 1.08 1.03 0.84 1.03 0.84 60.93 60.93 1.08 0.00 1.08 0.00 1.03 0.84 1.03 0.84 0.93 1.14 0.93 1.14 1.08 0.00 1.08 0.00 0.84 0.74 0.84 0.74 0.93 1.14 0.93 1.14 0.00 0.75 0.00 0.75 0.84 0.74 0.84 0.74 1.141.14 0.00 0.75 0.00 0.75 0.7 West South East North

11 11 Floor 15 G 15

1.02 1.02 11 11 1.07 1.07 1.021.02 11 0.92 11 0.92 1.071.07 1.02 1.05 1.02 1.05 11 0.92 11 0.92 1.07 0.00 1.07 0.00 1.02 1.05 1.02 1.05 0.92 1.13 0.92 1.13 1.07 0.00 1.07 0.00 1.05 0.74 1.05 0.74 0.92 1.13 0.92 1.13 0.00 0.75 0.00 0.75 1.05 0.74 1.05 0.74 1.131.13 0.00 0.75 0.00 0.75 0.7 12% 20% 12% 20% 12% 20% 12% 20% 1.01 1.01 15 15 1.06 1.06 1.011.01 15 0.91 15 0.91 1.061.06 1.01 1.04 1.01 1.04 15 0.91 15 0.91 1.06 0.00 1.06 0.00 1.01 1.04 1.01 1.04 0.91 1.12 0.91 1.12 1.06 0.00 1.06 0.00 1.04 0.73 1.04 0.73 0.91 1.12 0.91 1.12 0.00 0.74 0.00 0.74 1.04 0.73 1.04 0.73 1.121.12 0.00 0.74 0.00 0.74 0.7 1.16 1.21 1.08 0.71 0.00 1.27 0.86 0.87

193 19

1.13 1.13 19 19 1.16 1.16 1.131.13 19 1.06 19 1.06 1.161.16 1.13 1.17 1.13 1.17 19 1.06 19 1.06 1.16 0.00 1.16 0.00 1.13 1.17 1.13 1.17 1.06 1.23 1.06 1.23 1.16 0.00 1.16 0.00 1.17 0.79 1.17 0.79 1.06 1.23 1.06 1.23 0.00 0.86 0.00 0.86 1.17 0.79 1.17 0.79 1.231.23 0.00 0.86 0.00 0.86 0.7 1.04 1.09 0.93 1.07 0.00 1.15 0.81 0.76

1.03

1.08

0.93

0.84

0.00

1.14

0.74

0.75

1.02

1.07

0.92

1.05

0.00

1.13

0.74

0.75

1.01

1.06

0.91

1.04

0.00

1.12

0.73

0.74

1.13

1.16

1.06

1.17

0.00

1.23

0.79

0.86

Figure 83. Annual heat gain through windows per unit area for different directions 99

4.3.4 d Iteration 3: Increasing the projection factor with the context The projection factor of the windows facing west was increased to 0.83 and simulated along with the context as the western facade is exposed to the sun. The other windows were left untouched. A point to note is that only horizontal shading devices were explored in the study. Both the following simulations were done with respect to the base case where the eastern facade does not have any openings and hence, the projection factor was not applied to it. In the second simulation, the projection factor of the windows facing the buffer space, i.e., the ones facing north and south was decreased to 0.23 to check its effect on the energy consumption. A lot of resources are involved in the making of buildings and if there is even a slight chance to save some, then one should look for such opportunities. Additionally, all the iterations are accompanied by values for heat gain through windows and these values are present as heat gain per unit area of the window for the sake of better comparison. (i) Case 1_Increasing the projection factor to 0.8 only in the western facade (ii) Case 2_Increasing the projection factor to 0.83 in the western facade and decreasing the projection factor to 0.23 in the northern and southern facades

Annual heat gain per unit area (GJ)

14000 1.2 13000 12000 1.1 11000 1.0 10000 9000 0.9 8000 0.8 7000 6000 0.7 5000 0.6 4000

23 0.5 Series2

36 0.83 Series3

4 11

5 15

6 19

0.83 + 0.23 Series4

Figure 84. Comparison of heat gain per unit area of the windows facing south WEST FLOOR G 3 6 11 15 19

0.5 0.78 0.64 0.64 0.64 0.71 1.13

0.83 0.78 0.63 0.64 0.64 0.68 1.08

NORTH 0.83 + 0.23 0.78 0.63 0.64 0.64 0.68 1.08

FLOOR G 3 6 11 15 19

0.5 0.77 0.64 0.64 0.64 0.63 0.81

0.83 0.77 0.64 0.64 0.64 0.63 0.81

0.83 + 0.23 0.78 0.64 0.64 0.64 0.64 0.82

Figure 85. Comparison of heat gain per unit area of the windows facing west and north 100 High-rise neighbourhood

Bedroom 2

Living

Kitchen

Dining Bedroom 3

Bedroom1 Balcony

Existing Projection Factor

: 0.50

(In all directions) Proposed Projection Factor

: 0.83

With the increase in the length of the shading devices, a slight decrease can be seen in the heat gain through windows facing west, north and south. The same can be said about the cooling loads. This prompts for further interventions like vertical shading devices or any other system which can bring about a higher decrease in the heat gain. Furthermore, with the decrease in the projection factor on north and south, there is hardly any change in the heat gain through windows and there is a small change in the cooling loads which can be safely ignored. Hence, the projection factor can be decreased safely on the buffer zones to save resources, especially in large scale projects like Rainbow Vistas where a small change can have cascading effects on material and energy consumption. There is a massive jump in the cooling load from the ground floor to the third floor and it remains almost constant until fifteenth floor and has a sudden drop in the last floor. The same trend is seen in both the iterations. Ground floor has the least consumption, the last floor is almost its double while all other intermediate floors are the double of the nineteenth floor’s consumption.

Unit-wise annual cooling load (kWh) FLOOR G 3 6 11 15 19

0.50 FLOOR 2311.1 G 9822.2 3 9766.7 6 9677.8 11 9880.6 15 4088.9 19

0.83 0.50 2308.3 2311.1 9819.4 9822.2 8767.5 9766.7 9669.4 9677.8 9847.2 9880.6 4050.0 4088.9

Percentage decrease in cooling load

0.83 + 0.23 0.83 + 0.23FLOOR 0.83 G 2308.3 2308.3 2308.3 FLOOR G 3 9827.8 9819.4 9827.8 3 6 9772.2 8767.5 9772.2 6 11 9680.6 9669.4 9680.6 11 15 9869.4 9847.2 9869.4 15 19 4077.8 4050.0 4077.8 19

0.83 0.83 0.1

0.83 + 0.23 0.830.1 + 0.23

0.1 0.0 0.0 10.2 10.2 0.1 0.1 0.3 0.3 1.0 1.0

0.1 -0.1 -0.1 -0.1 0.0 0.0 0.1 0.1 0.3 0.3

Figure 86. Comparison of unit-wise annual cooling loads (kWh) for different iterations and their percentage decrease 101

4.3.4 e Iteration 4: Increasing the projection factor without the context It would be interesting to observe the change in the behaviour of shading devices without the surrounding buildings. The two cases in this iteration explore this possibility. In the first case, the projection factor of the windows facing west was changed to 0.83 while the others were retained as they were in the base case. In the next case, the projection factor of all the windows were uniformly increased to 0.83 so as to study its effect on the cooling loads. (i) Case 1_Increasing the projection factor to 0.83 only in the western facade (ii) Case 2_Increasing the projection factor to 0.83 uniformly for all facades

Annual heat gain per unit area (GJ)

Figure 81 shows the data regarding heat gain per unit area of the windows on the east and west facades. A gradual decrease in heat gain can be seen from third floor to fifteenth floor in most of the cases. The ground floor and the last floor however continue to have a peak in all the cases except for the western facade of the third and the fifteenth floors. A combination of different shading devices and window glazing might help decrease the heat gain through windows especially in the west facade. 1.40 1.20 1.40 1.00 1.20 0.80 1.00 0.60 0.80 0.40 0.60 0.20 0.40 0.00 0.20 0.5 W 0.00

E 0.5

0.83 W + 0.50 G

+ 0.50 60.8311 15

3 G

0.83 W 0.83

Figure 87. Comparison of heat gain per unit area of the windows facing west and east NORTH FLOOR G 3 6 11 15 19

0.5 0.86 0.81 0.74 0.74 0.73 0.79

0.83 + 0.50 0.84 0.72 0.72 0.72 0.71 0.83

SOUTH 0.83 0.83 0.71 0.71 0.70 0.70 0.82

FLOOR

0.5

0.83 + 0.50

0.83

G 3 6 11 15 19

1.08 0.93 0.93 0.92 0.91 1.06

1.04 0.90 0.89 0.89 0.88 0.83

0.95 0.81 0.80 0.80 0.79 0.82

Figure 88. Comparison of heat gain per unit area of the windows facing north and south 102 High-rise neighbourhood

Bedroom 2

Living

Kitchen

Dining Bedroom 3

Existing Projection Factor

Bedroom1

: 0.50

(In all directions)

Balcony

Proposed Projection Factor

: 0.83

As seen in Figure 54, the cooling load in both cases remains close to the base case except for the sixth floor where a slight increase of 8% can be observed. The first case yields a decrease of 1.3% to 2.8% in the cooling loads while the case of uniform projection factors yields a decrease of around 1.9% to 3.9%. The intermediate floors have similar consumption patterns while the ground and topmost floors see a drastic dip in their annual energy consumption as seen in earlier iterations. 14000 13000

Energy (kWh)

12000 11000 10000 9000 8000 7000 6000 5000 4000

Series2

Unit-wise annual cooling load (kWh) FLOOR

0.50

0.83 + 0.50

0.83

G 3 6 11 15 19

4488.9 13108.3 12471.2 12691.7 13288.9 5111.1

4361.1 12938.9 13505.6 12527.8 13086.1 5011.1

4313.9 12852.8 13422.2 12447.2 13011.1 4980.6

11 Series3

Series4

Percentage decrease in cooling load FLOOR G 3 6 11 15 19

0.83 + 0.50

0.83

2.8 1.3 -8.3 1.3 1.5 2.0

3.9 1.9 -7.6 1.9 2.1 2.6

Figure 89. Comparison of unit-wise annual cooling loads (kWh) for different iterations and their percentage decrease 103

104

Chapter 5 Conclusion

5.1 Comparison of the iterations 5.2 Future scope and creation of a framework 5.3 The way ahead

105

Go to Contents

5.1 Comparison of the iterations 5.1.1 Iterations of the mid-rise neighbourhood Each iteration will be compared with the base case and tabulated for better comprehension. The simulations done with context and the ones without context will be compared for further inspection. 5.1.1 a Shading devices in the presence of the surrounding context The study had analyzed the impact of changing the projection factor of the horizontal shading devices in different directions. The table below shows the projection factor applied to various directions in the two iterations; only the projection factor on the southern facade was increased without changing the others. 0.23

0.50

0.83

Base Case

Iteration 1

Iteration 2

North

0.00

South

0.23

0.50

0.83

East

0.23

West

0.23

Figure 90. Figure showing the projection factor applied for the two iterations

In both the iterations, as each unit had only two windows facing south, their projection factor was increased. Additionally, only one room in the unit was air-conditioned and hence the values of the cooling load are less. Clearly, the higher projection factor is working better in reducing the cooling loads by around 2%. The thermal zone with the window facing south sees a reduction in the cooling load as seen below.

FLOOR 0 1 2 3 4 5

0.23 37.01 152.91 177.96 182.26 182.26 203.94

0.5 35.70 150.10 175.15 179.45 179.45 201.32

0.83 35.14 148.42 173.28 177.58 177.58 199.64

Cooling load per unit area (kWh)

FLOOR 0 1 2 3 4 5

0.23 -

0.5 2.6 1.4 1.2 1.1 1.1 1.0

Percentage decrease in cooling load

Figure 91. Figure showing the projection factor applied for the two iterations 106 Conclusion

0.83 3.7 2.2 1.9 1.9 1.8 1.6

5.1.1 b Shading devices in the absence of the surrounding context The projection factor of all the windows was uniformly increased as shown in the table below. However, as the base case did not have windows in the north facade, the subsequent iterations also followed the same. 0.23

0.50

0.83

Base Case

Iteration 1

Iteration 2

North

0.00

South

0.23

0.50

0.83

East

0.23

0.50

0.83

West

0.23

0.50

0.83

Figure 92. Figure showing the projection factor applied for the two iterations FLOOR 0 1 2 3 4 5

0.23 -

WEST 0.5 8.8 12.8 13.8 14.1 14.1 14.1

FLOOR 0 1 2 3 4 5

0.23 -

0.5 10.0 14.0 15.3 15.7 15.6 15.8

0.83 15.0 21.7 23.7 24.1 24.1 24.0 0.83 17.0 24.0 26.1 26.6 26.6 26.3

Figure 93. Figure showing the % decrease in the heat gain per unit area (GJ) for West (Top) and East (Below) FLOOR 0 1 2 3 4 5

0.23 -

0.5 2.6 1.4 1.2 1.1 1.1 1.0

0.83 3.7 2.2 1.9 1.9 1.8 1.6

Figure 94. Figure showing the % decrease the cooling load 107

In both the iterations, the windows facing east and west are experiencing less heat gain per unit area as seen in the adjacent figure. The projection factor of 0.83 is performing better among the iterations with it reducing as high as around 26% heat gain in the east and 24% in the west. The reduction in the southern facade has been discussed in the previous chapter. In terms of cooling load, as the space that is air-conditioned has only one window facing south, all the reductions in the cooling load can be attributed to the same and clearly, the higher projection factor is performing the best by decreasing the cooling load by around 2%. The ground floor unit has benefited the most with a decrease of 3.7% of its cooling load.

5.1.1 c Increase in WWR in the presence of the surrounding context As discussed in the previous chapter, in order to increase the amount of natural light into the spaces, the WWR was increase in the iteration. This was achieved by increasing the width of the windows by 50% while retaining their height. Consequently, the gross WWR increased from 8.5% to 15.9% as shown below.

North South East West Gross WFR

8.5 %

16 %

Base Case

Iteration 1

0.0% 24.9% 3.8% 5.1% 8.5% 10.3%

8.4% 28.6% 11.5% 15.3% 15.9% 18.6%

Figure 95. The WWR for different directions

In the previously blank northern facade, windows were introduced for the sake of the study. The decision was reluctant because, the unit had a corridor running to its north. Any opening provided there would inevitably be shut with curtains or blinds for privacy from a passerby. The results of the simulation are in coherence with the literature study as the cooling loads have increased with the increase in the WWR. The intermediate floors seem to require a larger cooling load than 747.2 2952.8 295.2 0 the upper floors with an increase of 3455.6 6636.1 92.0 1 90%. This is also backed by the fact 4050.0 7772.2 91.9 2 that, out of the two thermal zones 4150.0 7341.7 76.9 3 of the air-conditioned room, with 4150.0 6825.0 64.5 4 the iteration, the western portion 4650.0 6500.0 39.8 5 needs a greater cooling load than Base 1Case Iteration 1 Base Case Iteration Figure 96. Figure showing cooling load its counterpart. Here, the western (kWh) and the % increase the cooling load 8.4% 0.0% 8.4%0.0%of the first and North North portion second floors Base 1Case Iteration 1 Base Case Iteration 28.6% 24.9% 28.6% 24.9% South South need the highest cooling while in 8.5 %.East 0.0% 163.8% %. East 8.4%0.0% 11.5% North North 3.8% 8.4% 11.5% the southern part, the fourth and 0 37.0 24.8 50.7 276.4 24.9%15.3% 28.6% South South 5.1% 28.6% 15.3% 8.5 %.West 24.9% 165.1% %. West 1 152.9 148.7 168.8 518.6 third8.5% floors need more 3.8% 15.9% 11.5% 11.5% East 8.5% East 15.9% cooling. In Gross Gross 0 37.0 24.8 3.8% 50.7 276.4 2 178.0 176.6 257.4 495.9 15.3% West 10.3% West the 10.3% base 15.3% case, both the thermal 18.6% WF R 5.1% 18.6% W F R 5.1% 1 152.9 148.7 168.8 518.6 3 182.3 181.1 261.1 434.8 8.5% zones behave 15.9% closely except for 15.9% Gross 495.9 Gross 2 178.0 176.6 8.5% 257.4 10.3% 18.6% 10.3% 18.6% W F R W F R 4 182.3 181.1 270.7 352.1 3 182.3 181.1 261.1 434.8 the ground and first floor where 5 203.9 203.4 217.6 410.4 4 182.3 181.1 270.7 352.1 the southern portion needs slightly 5 203.9 203.4 217.6 410.4 higher cooling load probably due to the reflection from the surrounding Figure 97. Figure showing the cooling load per surfaces. unit area of the two thermal zones (kWh) 8.5 %.

108 Conclusion

16 %.

% increase

The table below shows the heat gain per unit area of the window for the base case. Out of the four cardinal directions, the windows from south receive a larger amount of heat while the windows on the ground floor receive the most heat followed by fourth floor and the other floors closely behind them. The mid-rise neighbourhood has a buffer zone of plants at the edge of the every building block which have grown to reach the height of the windows in the ground floor. With the use of a paving which reflects less heat or through the addition of lawns or soft ground, this value for the windows can be effectively reduced. A point to note here is that the windows facing east and west, which face the buffer spaces behave closely, even on different floors.

0 1 2 3 4 5

North

South

East

West

0.00

1.04 0.86 0.84 0.86 0.96 0.88

0.64 0.31 0.25 0.24 0.23 0.23

0.68 0.31 0.25 0.23 0.23 0.24

0.00 0.00 0.00 0.00 0.00

Figure 98. Figure showing the heat gain per unit area (in GJ) of the windows facing different directions for the base case

0 1 2 3 4 5

North North

South South

East East

West West

0.640.64

1.36 1.36 1.13 1.13 1.62 1.62 1.64 1.64 2.63 2.63 0.85 0.85

0.52 0.52 0.37 0.37 0.34 0.34 0.36 0.36 0.51 0.51 1.39 1.39

2.41 2.41 1.90 1.90 1.58 1.58 1.20 1.20 1.05 1.05 1.29 1.29

0.300.30 0.240.24 0.230.23 0.250.25 0.290.29

Figure 99. Figure showing the heat gain per unit area (in GJ) of the windows facing different directions for the iteration

After the increase in WWR, the windows facing south and west receive comparable amounts of heat; apart from ground floor, the fourth floor receives most heat in the south facade, the last floor receives the most heat in the eastern facade and the first floor receives the most heat in the west facade. The windows facing north, however, have a smaller range of deviation amongst the different floors. Hence, one can conclude that a unit can have large openings facing north and the ones facing every other direction have to be shaded appropriately so as to cut down the heat gain from them and hence reduce the cooling loads.

109

5.1.1 d Increase in WWR in the absence of the surrounding context For this iteration, the WWR was uniformly increased to 16% and 20% through a plugin in the software. The software accordingly generated a series of ribbon windows on each facade as per the WWR input. Both these cases were simulated in isolation to analyze their behaviour. 8.5 %

16 %

20 %

Base Case

Iteration 1

Iteration 2

0.0%

16.0%

20.0%

24.9%

16.0%

20.0%

3.8%

16.0%

20.0%

5.1%

16.0%

20.0%

8.5%

16.0%

20.0%

10.3%

25.5%

31.9%

North South East West Gross WFR

Figure 100. The WWR for different directions and iterations

0 1 2 3 4 5

8.5%

16%

20 %.

900 3803 4392 4469 4447 4919

1522 4561 5111 5178 5153 5619

1747 4822 5350 5408 5381 5842

0 1 2 3 4 5

16%

20%

69 20 16 16 16 14

94 27 22 21 21 19

Figure 101. Figure showing the annual cooling load (in kWh) for the two iterations and their corresponding percentage increase

With the increase in the WWR, there is an increase in the cooling loads as seen in the table above. The ground floor sees a drastic increase in the cooling loads in both the cases. In the first iteration, the percentage increase varies from 14% to 20% with an average of 16%. In the second iteration, the range varies from 19% to 27% with an average of 22% increase from the base case. The cooling loads increase with the increase in the floor level with the top floor requiring the maximum cooling load. This could be a result of the direction radiation of the sun from the roof. The cooling loads from second to fourth floor closely follow each other. In the base case, the windows facing east and west directions behave closely with the eastern windows bringing in slightly more heat per unit area than the ones facing west. The windows facing south get in the maximum heat with the values of different floors close to each other. The base case did not have any windows on the north facade. However, in the two iterations, the windows facing north have the least heat gain compared to other windows and the values across the floors are close to each other. 110 Conclusion

0 1 2 3 4 5

North

South

East

West

0.00 0.00 0.00 0.00 0.00 0.00

1.10 0.91 0.87 0.87 0.97 0.87

1.21 0.84 0.76 0.74 0.74 0.75

1.16 0.81 0.73 0.71 0.71 0.71

Figure 102. Figure showing the heat gain per unit area (in GJ) of the windows facing different directions for the base case

0 1 2 3 4 5

North

South

East

West

1.31 0.73 0.61 0.58 0.58 0.59

1.36 1.06 1.02 1.33 1.20 1.14

1.05 0.90 0.87 0.86 0.85 0.86

1.22 0.98 0.94 0.92 0.92 0.92

Figure 103. Figure showing the heat gain per unit area (in GJ) of the windows facing different directions for the iteration 1

0 1 2 3 4 5

North

South

East

West

0.74 0.41 0.35 0.33 0.33 0.34

1.46 1.20 1.67 1.67 2.64 0.85

0.97 0.80 0.77 0.83 1.07 1.52

3.87 3.39 2.88 2.22 1.95 1.93

Figure 104. Figure showing the heat gain per unit area (in GJ) of the windows facing different directions for the iteration 2

With the increase in WWR, the heat gain through windows facing all directions except for the ones facing north increases. In the first iteration, the windows facing south bring in the most heat, followed by west and then east. The windows facing west bring in 27% more heat, the east ones bring in 14% more heat while the south ones bring in 28% more heat when compared to the base case. In the second iteration, the windows on the west facade bring in the most heat followed by the ones facing south and east. In the south facade, the fourth floor brings in the most heat while on the west, the first floor does. On an average, the windows facing west bring in 233% more heat, the east ones 31% more and the south ones 77% more heat when compared to the base case. 111

5.1.1 e Inference Based on simulations of the iterations, the following conclusions can be made. The study started with the idea of creating a framework for each component of a building envelope and how the same can be optimized through parameters to achieve energy efficiency for different scales of residential neighbourhoods. However, only two parameters were considered and analyzed for the study due to the time period of the research. There is a possibility for future expansion of the study and the creation of a framework, where more ancillary parameters can be added for a comprehensive analysis and set of inferences.

• With multi-storeyed buildings coming up in the empty plots surrounding the neighbourhood, the amount of natural light inside the units is bound to decrease, especially in the units on the lower floors, which are already devoid of sufficient natural daylight. Hence, the study involved increasing the WWR to cater to this situation. North is the most ideal direction to increase the WWR. The units could have their open spaces like balconies facing north and it would be better if buffer spaces like the corridors are not on the northern portion of a unit as the windows cannot be introduced there. • The projection factor of all the windows in the neighbourhood is currently 0.23. This corresponds to a length of 270 mm for the horizontal shading device in the window. An increase in the projection factor of the south-facing windows alone in unit studied for the paper can save upto 2% of the cooling load and is hence recommended for energy efficiency and reduction in the heat gain through windows. • A projection factor of 0.5 (corresponding to a length of 600 mm) is sufficient for the souther facade, the units exposed to east and west are recommended to have a projection factor of 0.83. (corresponding to a length of 1000 mm) Any PF beyond 0.83 would be cumbersome to construct in large scales. • An increase in the WWR is accompanied by an increase in the cooling loads. But, an increase combined with appropriate projection factor can decrease the cooling loads. A uniform WWR of 16% corresponds to a WFR of 25% and a WWR of 20% corresponds to a WFR of 32%. Hence, the lower floors which require more daylight could have a higher WWR ratio while the upper floors can have a lesser or the same ratio.

112

Conclusion

• The openings facing east, west or south should have lesser WWR, especially the intermediate floors i.e., from the second floor to the fourth floor; if the openings have to be provided inevitably in that direction, they must be accompanied by higher projection factors with a minimum of 0.5. • The WWR for a unit and the projection factor for a window should be guided by the orientation of the unit, its surrounding blocks and the face exposed directly to sun’s radiation instead of a uniform application of these parameters. For instance, in the unit selected, the south facing windows could have a PF of 0.83 while the others could have 0.23 as they are facing a buffer zone. This would not only save resources but would also positively impact the energy consumption of the unit. • Additionally, an increase of 70% is seen in the cooling loads when 80% of the unit is air-conditioned as opposed to the base case of 20%. • The unit on the top floor required more cooling loads when compared to the other floors. Installation of an additional shading surface on the roof like a solar panel might help in the reduction of the radiant heat while also making the neighbourhood self-sufficient energy-wise.

Mid-rise neighbourhood Window to wall ratio North - Maximum WWR South - Lesser WWR East & West - Least WWR WWR for each block should be subject to local conditions and shading surfaces Windows have to be accompanied with appropriate shading devices as given in the adjacent column

113

Projection Factor East & West - Minimum PF 0.5 (Additional shading devices like vertical surface would improve performance) South - Minimum PF 0.23 North - Minimum PF 0.23 Each facade could have a unique PF according to the orientation and surrounding context

5.1.2 Iterations of the high-rise neighbourhood 5.1.2 a Shading devices in the presence of the surrounding context As the western facade was exposed to direct radiation from the sun, its projection factor was increased without changing the others. However, in the second iteration, the projection factor of the windows facing the buffer spaces was reduced to observe their implication on the cooling loads. The eastern facade is devoid of any windows and hence there is no change in their projection factor. 0.50

0.83

0.83 + 0.23

Base Case

Iteration 1

Iteration 2

North

0.50

0.23

South

0.50

0.23

East

0.00

West

0.50

0.83

Figure 105. Figure showing the projection factor applied for the two iterations

In both the iterations, as each unit had only two windows facing west, their projection factor was increased. In both the iterations, the heat gain per unit area of the west facing windows sees a negligible amount of difference after increasing the projection factor. FLOOR G 3 6 11 15 19

0.5

0.83

0.83 + 0.23

0.78 0.64 0.64 0.64 0.71 1.13

0.78 0.63 0.64 0.64 0.68 1.08

Figure 106. Figure showing the heat gain per unit area of the windows (GJ) facing west across different iterations

FLOOR G 3 6 11 15 19

0.83

0.83 + 0.23

0.1 0.0 0.0 0.1 0.3 1.0

0.1 -0.1 -0.1 0.0 0.1 0.3

Figure 107. Figure showing % decrease in the iterations

This might call for additional interventions in the west facade for a further decrease in cooling loads. Similarly, the decrease in cooling load is also negligible. However, the decrease in projection factor on the buffers sides also amounts to a small change in the cooling load. Hence, it can be concluded that the projection factor of windows in the buffer spaces can be safely reduced without affecting the energy consumption as the windows are already mutually shaded. 114 Conclusion

5.1.2 b Shading devices in the absence of the surrounding context In this iteration, unlike the base case, windows were introduced in the eastern facade to observe their behaviour without the context. The first iteration involved changing the projection factor of only the east and west facing windows while the second one involved uniformly increasing the projection factor on all facades. Base Case

Iteration 1

Iteration 2

North

0.50

0.83

South

0.50

0.83

East

0.50

0.83

West

0.50

0.83

Figure 108. Figure showing the projection factor applied for the two iterations

In both the iterations, there is a small decrease in the cooling load. The second iteration can be considered better as it yields more decrease in the cooling load. However, the sixth floor experiences an increase in the cooling load. In the case of heat gain per unit area of the windows, the windows in the north facade have negligible change. The ones facing west have about 6% decrease and the windows on the east have a slightly higher decrease of about 10% in their heat gain. The windows on the sixth and third floors facing FLOOR Iteration 1 Iteration 2 west experience an increase in the heat gain and G 2.8 3.9 this could be attributed to the sun’s low angles 3 1.3 1.9 6 -8.3 -7.6 in the evenings. The variation in heat gain across 11 1.3 1.9 the floors is negligible . 15 19

1.5 2.0

2.1 2.6

Figure 109. % decrease in the iterations Base Case FLOOR G 3 6 11 15 19

FLOOR Iteration Case1 Iteration 2 Iteration 1 Iteration 2Base G WEST 2.8 3.9 FLOOR EAST WEST0.83 EAST EAST WEST EAST FLOOR FLOOR 0.5 0.83 0.5 + 0.50 0.83 +0.83 0.50 3 1.19 1.3 1.09 1.04 1.9 1.11 0.95 0.99 0.99 G G1.11 1.08 0.991.08 G 1.04 0.95 6 -8.3 -7.6 3 0.87 3 0.97 0.93 0.870.93 3 1.04 0.90 0.98 0.90 0.81 0.97 0.81 0.87 11 1.3 1.9 1.25 0.80 0.87 6 1.03 0.97 0.87 6 1.25 0.93 0.870.93 6 0.89 0.89 0.80 15 1.02 1.5 0.96 0.89 2.1 0.95 0.80 0.86 0.86 11 110.95 0.92 0.860.92 11 0.89 0.80 19 2.0 2.6 1.25 0.79 0.85 15 1.01 0.95 0.85 15 1.25 0.91 0.850.91 15 0.88 0.88 0.79

Iteration 1

WEST

EAST

WEST

1.19 1.04 1.03 1.02 1.01 1.15

1.09 0.98 0.97 0.96 0.95 1.06

1.11 0.97 1.25 0.95 1.25 1.08

0.97 19 191.08 1.06 0.971.06 19 1.15 0.83 1.06 0.83 0.82 1.08 0.82 0.97

Figure 110. Heat gain per unit area of the windows (GJ) facing west, east and south 115

5.1.2 c Increase in WWR in the presence of the surrounding context As discussed in the previous chapter, in order to increase the amount of natural light into the spaces, the WWR was increased in the iteration. This was achieved by increasing the width of the windows by 50% while retaining their height. Consequently, the gross WWR increased from 11.8% to 16.9% as shown below. Additionally, windows were introduced in the east facade to observe their behaviour. 11.8 %

16.9 %

12.2%

17.2%

6.1%

8.6%

0.0%

8.5%

29.1%

33.2%

11.8% 19.1%

16.9% 28.1%

Base Case Iteration 1 North South East West Gross WFR

Figure 111. The WWR for different directions

With the increase in WWR, the cooling loads have increased by an average of 8% excluding the top and the ground floor. The top floor seems to have a lesser cooling load and it is assumed that the inputs that the software has analyzed are ambiguous and hence, for the study, the values have been considered until the fifteenth floor. The ground floor has the least consumption due to a location advantage.

0 3 6 11 15 19

11.8 %.

16.9 %.

% increase

2311 9822 9767 9678 9881 4089

2989 10644 11111 10483 10275 4950

29.3 8.4 13.8 8.3 4.0 21.1

Figure 112. Figure showing cooling load (kWh) and the % increase the cooling load

A point to note here is that 80% of the unit is air-conditioned and hence, its annual consumption is a five-digit figure as the software considers the maximum operational hours. In the base case, the fifteenth floor and the third floor have the maximum cooling load followed by the sixth and eleventh floor while in the iteration, the sixth floor consumes the highest energy and is followed by third, eleventh and fifteenth floor. This fact proves that all the intermediate floors, from the third to fifteenth floors could behave similarly (cooling load-wise) and are equally vulnerable to the sun’s radiation unless they are shaded by another block. 116 Conclusion

0 3 6 11 15 19

North

South

East

West

0.77 0.64 0.64 0.64 0.63 0.81

0.80 0.65 0.65 0.65 0.66 0.99

0.00 0.00 0.00 0.00 0.00 0.00

0.77 0.65 0.65 0.65 0.72 1.11

Figure 46. Figure showing the heat gain per unit area (in GJ) of the windows facing different directions for the base case

0 3 6 11 15 19

North

South

0.76

0.78

0.63

0.64

0.63

0.64

0.63

0.64

0.62 0.78

0.64 0.96

East 0.76 0.64 0.64 0.64 0.64 1.01

West 0.75 0.64 0.93 0.64 1.05 1.08

Figure 113. Figure showing the heat gain per unit area (in GJ) of the windows facing different directions for the iteration

Contrary to the findings in the mid-rise neighbourhood, the values of heat gain per unit area of the windows facing north and west are very close in the base case with the fifteenth floor and the top floor having slightly grater heat gain. Interestingly, in the intermediate floors, the windows facing south behave similar to each other with an exception of the ground and topmost floor. Similar to Hallmark Tranquil, Rainbow Vistas also has a buffer zone of plants at the boundary of each block and a decent amount of pervious surface which help in reducing the reflected heat from the surfaces which are causing the heat gain values of the ground floor shoot up. In the iteration, with the increase in WWR, the heat gain values of windows facing different directions are very similar to each other with the exception of the last floor and a spike in the heat gain of the windows facing west in the sixth floor. The results show that the fenestrations have to be treated accordingly to reduce heat gain through them and hence the cooling load.

117

5.1.2 d Increase in WWR in the absence of the surrounding context In the first case of the iteration, the gross WWR was increased to 16.9% by changing the width of the windows by 50% while retaining their height. Additionally, windows were introduced in the east facade to observe their behaviour. For the second case of the iteration, the WWR was uniformly increased to 20% using the software plug-in which creates proportionate ribbon windows on each facade. 11.8 %

Base Case North South East West Gross WFR

16.9 %

20.0 %

Iteration 1 Iteration 2

12.2%

17.2%

20.0%

6.1%

8.6%

20.0%

0.0%

8.5%

20.0%

29.1%

33.2%

20.0%

11.8% 19.1%

16.9% 28.1%

20.0% 29.6%

Figure 114. The WWR for different directions

With the increase in WWR in iteration 1, the cooling loads have increased by an average of 10.1% excluding the top and the ground floor. The iteration 2 has an increase of around 10.8% in the cooling loads. The ground floor has the least consumption due to a location advantage in all the iterations. The three cases reach their peak in different intermediate floors as seen below.

0 3 6 11 15 19

11.8%

16.9%

20%

3553 11994 11825 11592 11433 4192

4489 13108 12471 12692 13289 5111

4569 13278 13094 12844 12669 5158

Iteration 1 Iteration 2 0 3 6 11 15 19

26.3 9.3 5.5 9.5 16.2 21.9

28.6 10.7 10.7 10.8 10.8 23.1

Figure 115. Figure showing cooling load (kWh) and the % increase the cooling load

In the base case, the peak in cooling loads is achieved in the third floor and it decreases with subsequent floors. In the first iteration, the fifteenth floor and the third floor have the most consumption followed by the other intermediate floors while in the last iteration, the peak is in the third floor and the cooling load reduces as the floor level increases. The consumption of the third floor can be traced to the sun’s low angles incident on the facade and hence being responsible for the surge in the cooling loads. With the help of shading buffers like vegetation, one can try and shade up to four floors of a building. 118 Conclusion

0 3 6 11 15 19

North

South

East

West

0.86 0.81 0.74 0.74 0.73 0.79

1.08 0.93 0.93 0.92 0.91 1.06

0.00 0.00 0.00 0.00 0.00 0.00

1.16 1.04 1.03 1.02 1.01 1.13

Figure 116. Figure showing the heat gain per unit area (in GJ) of the windows facing different directions for the base case

0 3 6 11 15 19

North

South

East

West

0.84 0.72 0.72 0.71 0.71 0.83

1.04 0.90 0.89 0.89 0.88 1.02

1.09 0.98 0.97 0.96 0.95 1.06

1.14 1.02 1.48 1.00 1.50 1.10

Figure 117. Figure showing the heat gain per unit area (in GJ) of the windows facing different directions for the base case

0 3 6 11 15 19

North

South

East

West

0.87 0.76 0.75 0.75 0.74 0.86

0.71 1.07 0.84 1.05 1.04 1.17

1.27 1.15 1.14 1.13 1.12 1.23

1.21 1.09 1.08 1.07 1.06 1.16

Figure 118. Figure showing the heat gain per unit area (in GJ) of the windows facing different directions for the base case

In the base case, the heat gain through windows was maximum through west, followed by south and north. The values for the intermediate floors still close to each other. However, with the introduction of windows in the east, the order of heat gain decreases from west to east to south to north facade. In the final iteration, the eastern windows bring in more heat than compared to the ones facing west. An interesting point to note here is that the values of heat gain through the windows facing north remain almost the same throughout the two iterations. This reassures the previous finding of the north direction being the ideal orientation to increase the WWR in the mid-rie neighbourhood.

119

5.1.2 e Inference Based on simulations of the iterations, the following conclusions can be made. There is a possibility for future expansion of the study and the creation of a framework, where more ancillary parameters can be added for a comprehensive analysis and set of inferences.

• The minimum Window-to-Floor ratio required for a building in a composite climate is 10%. The WFR for the neighbourhood is 19% with a gross WWR of 11.8%. • In the neighbourhood, the units in each block are arranged on either side of a spinal corridor facing the North-South orientation. As the aspect ratio of the blocks is high, they mutually shade each other; this also means that the amount of natural light entering the units is reduced even further. This can be seen from the lux levels of the ground floor unit. Hence, the study involved increasing the WWR to cater to this situation. North is the most ideal direction to increase the WWR similar to the mid-rise neighbourhood. • The projection factor of all the windows in the neighbourhood is currently 0.5. This corresponds to a length of 600 mm for the horizontal shading device in the window. In the unit studied, there is a negligible difference when the PF of the windows facing west is increased to 0.83. This is an indication that the existing PF is barely working and other methods of shading should be explored to reduce the heat gain through the windows and hence the cooling load. • An interesting point to note is that, when the PF of the windows facing the buffer between is reduced to 0.23, there is minimal change in the cooling load and hence it can be safely assumed that those windows do not need a PF of 0.5 as the blocks already create mutual shading effect there.

120 Conclusion

• The current WWR is insufficient to bring in natural light into the units, especially in the lower floors of the blocks; this creates a need for an increase in the WWR. As the increase in WWR is coupled with the increase in the cooling loads and the horizontal PF of 0.83 is barely sufficient as a uniform increase in the PF to 0.83 yields a reduction of around 3% of cooling loads, other aspects of the fenestration need to be explored to increase the WWR in the directions of east, west and south. • Similar to the previous neighbourhood, the WWR and PF have to be designed keeping in mind the local conditions, with further exploration in parameters which can help reduce the cooling loads and the heat gain through windows.

High-rise neighbourhood

Window to wall ratio North - Maximum WWR South - Lesser WWR East & West - Least WWR

121

Projection Factor

WWR for each block should be subject to local conditions and shading surfaces

East & West - Minimum PF 0.5 Recommended 0.83 (Additional shading devices like vertical surface would improve performance) South - Minimum PF 0.23 Recommended 0.50 North - Minimum PF 0.23

Windows have to be accompanied with appropriate shading devices as given in the adjacent column

Each facade could have a unique PF according to the orientation and surrounding context

5.2 Further scope and creation of a framework In the study, only two parameters were considered for the study keeping the others as constants due to various challenges. The parameters are dependent on each other and a comprehensive study of all the components can only lead to their optimization. Hence, the study can be seen as the beginning of the formation of a framework which can be expanded over time as data and analysis of other parameters are fed into it. The study here was done across three neighbourhoods while looking at two parameters. The study can be continued further where one neighbourhood is studied throughly through various parameters. All the simulations in the study considered artificially air-conditioned spaces; however, with the case studies, it was observed that the in the high-rise neighbourhood, especially in the upper floors, there was a great deal of natural winds and the same was not taken into consideration while it could play an important role in the internal operative temperatures of the unit. Future studies could incorporate such aspects which are observed on site and be used as parameters in the study. The study looked only at iterations involving an increase in WWR while not including possibilities involving a decrease in WWR; additional studies could be done where the WWR, the lux levels and the indoor temperatures and heat gains could be co-related and modified accordingly to produce a comprehensive understanding of each parameter. The study began with identifying the various components of a building envelope. The outer wall and its properties (physical and thermal) were considered constant as both the neighbourhoods had similar properties and surface finished. The WWR of the blocks was selected for analysis while the fenestration type was made a constant as both the neighbourhoods (mid-rise and high-rise) had similar kind of openings and window type. The various types of openings like a louvered window, an awning window or a clerestory window to name a few can also be studied. The glazing of the fenestration was also considered a constant as one seldom sees the use of a double glazing window in a residential neighbourhood unlike an office building. Out of the various shading devices presented, horizontal shading devices were studied as both the neighbourhoods had adopted them as overhangs while other options like a vertical shading device or an egg-crate system can also be studied. The heat gain through vertical surfaces like the wall and horizontal surfaces like the roof were not considered due to the short time period of the research.

122 Conclusion

Furthermore, the roofs of the neighbourhoods selected were not accessible and hence even the primary on-site data could not be collected from them. This would have shed some light on the electricity consumption patterns of the units on the last floor. The various ways in which facade treatment could also be explored; for instance a double skin or a jaali. The effect of these above mentioned components can be studied not just with the lens of energy but also along with the amount of daylighting, thermal and visual comfort inside the spaces. Similarly, various combinations of these parameters can be applied to different on-site conditions to eventually achieve energy efficiency. The factor of user behaviour is a tricky yet worthwhile area of study, and can factor into the research for better results. The simulations can be carried across all the three types of neighbourhoods to observe the behaviour of these parameters across different scales of residential typologies. An attempt has been made to initiate this process as seen in the following page.

123

Typology of neighbourhood

10%

15% 20%

E W

Window to Wall Ratio

Surface Treatment

% CHANGE IN ENERGY CONSUMPTION APPLICATION AT DIFFERENT SCALES

Wall Thickness Materiality

Envelope Properties Type of opening Physical Dimensions Glazing N

Operability

0.23

Fenestration Horizontal Vertical

Type

Others

Physical and thermal properties

Shading Devices

Amount of Porosity Jaali/ Double Skin Other intervention

Facade Treatment Internal heat gain Daylight and Lux Levels Comfort level

Thermal & Visual Comfort

124 Conclusion

Projection 0.50 Factor 0.83

E W

5.3 The way ahead The recent events of recurring heat waves in India has made our inevitable dependence on artificial cooling crystal clear and the situation would only get dire in the years to come. The study was carried out with the intention that over the years, a framework of sorts would be used by designers, urban planners or anyone responsible for the making of a built environment to inform design decisions. The Episode 1 of this paper dealt with the enhancing of the macro-scale parameters in achieving thermal comfort and energy efficiency in residential neighbourhoods. Similarly, many such episodes can be added as continuation to this inquiry so as to expand the framework to cater to a larger user group and a range of situations and climatic configurations. The study could also be inter-disciplinary so as to include the various stakeholders (from a lay person to a designer to an artisan to a electric-technician to the client, to name a few) involved in the making of built environments. The study hopes to see many energy efficient neighbourhoods in the years to come.

Figure 119. (Left) A framework for analyzing and optimizing parameters of a building envelope to achieve energy efficiency 125

126

Appendix i. Online user survey A unique survey was circulated to each neighbourhood with a similar theme and below is a glimpse of the survey of the high-rise neighbourhood created using Google forms. Section 1 below contains questions on comfort.

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Go to Contents

128 Appendix

i. Online user survey (continued) Section 2 below contains questions on energy consumption patterns across the year and the type of appliances in the residential unit with a focus on the number of airconditioners and their star rating.

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At the end of each section, an optional descriptive type question was asked where the users could fill in their thoughts, suggestions or any other information that they would like to share. The users filled in very interesting and insightful information which has been included in the previous sections. I would like to express my gratitude to two platforms: Google-forms and Visme which have made the analysis of the survey results very convenient. Without these platforms, we wouldn’t be able to present the data collected in the surveys in an adequate manner. ii. Block modelled for simulation As mentioned earlier, the neighbourhoods were simulated using OpenStudio. The entire block is generated from a line diagram that can be seen in the roof of the figures below. On the adjacent page, the base case is shown along with the iteration where the WWR has been increased using the plug-in in the software. Ribbon windows are generated through the plug-in as shown in the figure. 130 Appendix

The figures above show the condition of the mid-rise neighbourhood while the ones below show the high-rise neighbourhood. The plug-in for WWR generates windows of the specified value onto the surfaces selected. Here, all the facades are treated the same while ignoring the on-site conditions of a duct or balcony area; they can be considered in further studies and can be detailed accordingly.

Figure showing the blocks modelled for simulation 131

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Achieving energy efficiency through optimization of components of a building envelope A framework towards energy efficient neighbourhoods Episode 2 A research by C M Sanandana | CEPT University Guided by Prof. Lilly Rose A 138