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DATA MINING & SMART LIGHTING SOLUTIONS

Analyzing insights from installed projects to increase customer adoption White paper


1. Introduction Smart Lighting Solutions (SLS), which rely on sensor networks, capture an immense amount of data. To fully leverage this data, users should analyze and manage the data as a strategic asset. One powerful feature of SLS is the ability to report energy consumption data in short time sets. The granular nature of this data affords the buildings operator an opportunity to modify occupant behavior to achieve savings goals. Far from “set it and forget it,” the data captured from actual building use is dynamic and changes over time. When properly analyzed, datasets from SLS produce better energy consumption outcomes. These outcomes lower operating costs, which frees up cash flow and helps mitigate the financial risks associated with the initial capital expenditure. AEP Ohio, like its customers, learns from and adapts its SLS program based on the program data it collects. The AEP Ohio SLS Pilot program has operated since 2014 and paid incentives for networked lighting controls in over 6 million square feet of diverse building space. Regardless of the building type, the results are the same – LED lighting and networked lighting controls save AEP Ohio about 116 million kWh annually. In addition to energy savings, SLS create additional value for both the customer and the utility. The goal of AEP Ohio’s SLS program is to help its utility customers adopt the most efficient technology, which maximizes kWh savings. First, we will define SLS and show how it is a stepping stone to the Internet of Things (IoT). We will analyze real-world case studies to show how some early adopters have capitalized on these changes to dramatically reduce their energy consumption. Additionally, these real-world adopters discovered secondary benefits from their networked lighting system, which have increased the comfort of their occupants and lowered maintenance costs. Finally, we will discuss a specific measure that will help building operators make better use of the dynamic nature of the data collected by a SLS system. Verifying the energy savings data collected by a SLS system mitigates financial risks. Using data to prove operational savings helps grow the appeal of networked lighting systems and allows financial officers to make better decisions.

1.1 Executive summary Commercial office spaces, manufacturing facilities, schools, and warehouses of all sizes are excellent candidates for SLS systems. With commercial building types, it is critical that an accurate baseline be established before funding for a project can be reserved. Engineering walkthroughs early in the application process help determine the facility’s existing energy conservation methods, like time clocks, and allow auditors an opportunity to interview staff to establish the building’s occupation schedule. For industrial building types, engineering walkthroughs are preferred to help anticipate potential changes to a space, but baselines are more easily established because most facilities operate on a 24/7 schedule. Typical fixtures for commercial spaces, like 3-lamp, T8, 2x4 foot troffers, consume less energy per fixture than their industrial counterparts, that typically rely on higher wattage high bay lighting. While most office space is densely populated and constantly occupied during normal operating hours, industrial facilities employ less people per square foot, and other areas, such as cold storage, are often unoccupied. Additionally, most commercial spaces operate on an 8 a.m.-5 p.m. schedule while industrial building types operate on a continuous schedule. The effect of these differences produces higher ROI for industrial projects and lower ROI for commercial projects.


To help mitigate the effect of building type and usage on a SLS incentive, utilities should provide commercial building types with a higher incentive. Industrial facilities have a higher savings potential and therefore need less of an incentive to drive adoption. Providing an incentive based on a facility’s square footage, regardless of primary use, helps simplify the application process and drives more projects into the program pipeline.

2. What are Smart Lighting Solutions? Lighting is moving beyond solely providing illumination. Smart lighting refers to illumination that is tailored to meet specific work requirements while consuming the least amount of energy possible. To achieve this the luminaire and control act as a system. The systemic relationship between the luminaire and sensor is the fundamental building block of smart lighting solutions. Smart lighting systems form digital networks comprised of luminaires, sensors, software, and servers. These individual products communicate using a set protocol over a physical layer that is controlled by a central server. Individual devices receive a unique address, which allows for two-way communication between a controller and the device. Network topology can be hard-wired or wireless depending on preference. The bi-directional nature of digital networking makes real-time data capture possible. Servers store this data and advanced algorithms in software compile the data into useful information shared over a dashboard. The shift to LED technology is the primary driver establishing the systemic relationship between the luminaire and sensor. The U.S. Department of Energy forecasts that LED technology will be 80-100% of the marketplace by 2030,1 as shown in figure 1. LEDs are an inherently controllable digital technology and adoption of LED technology is positively correlated with an increase in SLS sales. In addition to providing high levels of efficacy, solid state lighting (SSL) is easy to shape, allowing designers who use spec grade products the ability to customize distribution patterns to create optimal visual environments. Incorporating best practices in lighting design saves additional energy and costs by eliminating unneeded fixtures before they are purchased.

1

http://energy.gov/sites/prod/files/2015/05/f22/energysavingsforecast14.pdf


Figure 1. US lighting forecast, 2013-2030

Once we start layering these technologies, the nucleus of an intelligent building system begins to emerge. Best practices in lighting design eliminate waste by only specifying products that meet the work requirements for a space. These SSL products easily integrate into a sensor network, which has been designed to capture the maximum amount of data. The digital illumination system can communicate bi-directionally over a physical layer to a server. The server runs sophisticated software that analyzes, optimizes, and stores data. Building operators can leverage this dynamic data to create unique scenes that maximize savings based on actual occupancy. Utilities achieve sustainable kWh savings without costly field visits for measurement and verification. Going further, SLS are a stepping stone to the Internet of Things. Here, all devices that consume energy will report data in real time to a single dashboard. Lighting is a perfect first step to transition to smart buildings because every building type employs artificial lighting. We use artificial light to illuminate our streets, houses, offices, parks‌ and the lighting is always over our heads; which is an ideal position for a sensor. For example, when you enter an office, a sensor detects occupancy. The overhead light powers to the appropriate ratio to balance natural to artificial light. The plug load control is lifted and energy flows freely to your outlets. If you are unhappy with any of the predefined settings, you can override the presets from your smart device to achieve a level of personal control. Once you vacate the space, the lights go dark and power stops flowing to the outlets. The above scenario is becoming increasingly tangible. The interconnection of thousands of smart devices will capture terabytes of data. To fully leverage this data to maximize energy savings, building operators must have a strategy in place for managing these datasets.


3. How do smart lighting solutions create value for customers and utilities? Verifiable, deep energy savings is the ultimate value that SLS creates for building operators and utilities. For a building operator, SLS produce predictable savings, which helps to better estimate future cash inflows from operational savings. Having a good estimate of future cash inflows is critical given the long lifecycle, up to 20 years, for SLS projects. Positive cash inflows lower lifetime ownership costs and make investments in SLS more attractive. For utilities, SLS help ‘future-proof’ savings by exceeding even the most stringent code, like California’s Title 24. These savings continue beyond the current program year and better positions utilities to meet future goals. Traditional widget-based lighting incentives lack the complexity to capture the savings from SLS accurately. Lighting best practices are moving the industry away from ‘one for one’ direct replacement, and progressive utilities, like AEP Ohio, are capitalizing on this trend by offering a stand-alone SLS incentive.

3.1 Overview of the AEP Ohio program AEP Ohio partnered with DNV GL in 2014 to create a Smart Lighting Solutions Pilot. The initial program payed $0.18 per kWh saved, and qualifying systems were required to purchase a lighting upgrade and a networkable control solution. The verification of these savings used the same standards as the custom program. AEP Ohio incentivized five projects in 2014 and found that verifying savings up front proved difficult using the standard custom program protocols. For 2015, AEP Ohio analyzed the data to reconfigure its incentive into three tiers. The desire for the rework was to capture as much of the installation cost as possible while simplifying the process. AEP Ohio decided on requiring projects to meet at least three of five control strategies: 1. Networking of luminaires 2. Occupancy sensing 3. Daylight harvesting 4. Software reconfigurable zoning 5. Continuous dimming Each project’s savings would undergo a custom review, creating an additional requirement that control systems report data in no more than 15-minute intervals. The data that these systems collected was required to be stored for at least one year. AEP Ohio defined the incentive tiers as the following. Tier 1: For buildings who have previously performed a lighting upgrade. This tier incentivized the purchase and installation of a SLS system. Tier 2: For buildings performing a lighting upgrade with dimmable fluorescent technology and purchase and installation of a SLS system. Tier 3: For buildings performing a LED lighting upgrade and purchase and installation of a SLS system.


Table 1. Smart lighting incentives per tier Building type

Incentive tier

Project incentive rate ($/ft2)

Low lumen high density (office) Low lumen high density (office) Low lumen high density (office) High lumen low density (warehouse/MFG) High lumen low density (warehouse/MFG) High lumen low density (warehouse/MFG)

1 2 3 1 2 3

$0.30 $0.65 $1.50 $0.15 $0.25 $0.45

Estimated $/kWh incentive cost $0.13 $0.15 $0.17 $0.09 $0.09 $0.11

A goal of reconfiguring the program was to test the appropriate size of the incentive relative to the kWh saved. AEP Ohio capped the maximum payout at 50% of the project’s total costs. As Error! Reference source not found. above shows, the incentive was expected to yield a range of costs from $0.09 per annualized kWh to $0.17 per kWh saved. For 2015, AEP Ohio incentivized a total of 16 projects. As AEP Ohio began processing applications a trend emerged. Though a variety of building types submitted applications, the lighting for all buildings fit neatly into two categories: high lumen low density (HLLD) and low lumen high density (LLHD). Broadly, HLLD projects are warehouse and industrial spaces with ceilings over 12 feet, while LLHD projects are commercial office buildings and education facilities. Table 2 highlights the project count by each type and incentives paid to kWh reduced. Table 2. Smart lighting solutions project review (2015) SLS control type High lumen low density Tier 3 Low lumen high density Tier 2 Low lumen high density Tier 3 TOTAL

Project count 8 3 5 16

Square feet

Incentives

kWh

896,689 164,989 161,620 1,223,298

$396,585 $99,269 $242,430 $738,285

2,073,413 327,647 696,842 3,097,902

For budget year 2016 to present, AEP Ohio furthered simplified the incentive program by dropping the tier structure in favor of classifying projects as either HLLD or LLHD. Table 3 shows the project count and savings from all 2016 projects and projects paid in the first quarter of 2017. Table 3. Smart lighting solutions project review (2016-2017, Q1) SLS type High lumen low density Low lumen high density TOTAL

Project count 16 18 34

Square feet 3,210,752 1,009,096 4,219,848

Incentives $636,912 $744,740 $1,381,652

kWh 3,883,212 1,373,157 5,256,369


The simplification of the program structure has also led to a lowering of the incentive based on past performance. For program year 2016, LLHD projects were paid at $0.75 per square foot and HLLD projects were paid at $0.30 per square foot, as shown in Table 4. The lowering of the incentives reflects the data, which shows that higher lumen fixtures at a lower density produce a higher percentage of energy savings than lower lumen fixtures at a higher density. Table 4. Smart lighting current incentive Installation type Lower lumen fixtures/higher fixture density (e.g., fixture height at or below 12 feet, office, classroom, etc.) Higher lumen fixtures/lower fixture density (e.g., fixture height above 12 feet, warehouse, gymnasium, etc.)

Project incentive rate ($/ft2) $0.75 $0.30

3.2 Challenges in determining baseline usage Accurate energy savings calculations are critical in determining proper incentive levels for SLS. Customer incentives are predetermined based on the type of space and total square footage while kWh savings are calculated using data driven custom analysis. As touched on earlier, one powerful feature of a SLS system is the ability to collect data in short time sets, which allow operators to observe usage patterns and refine the system to optimize savings. The challenge for SLS is accurately determining a baseline usage pattern for existing buildings. For new construction, the baseline power calculation (kW) is a simple equation based on the appropriate ASHRAE standard. However, for existing structures a baseline must be estimated. It is unrealistic to assume 24/7 operations for all building types. Other techniques, such as deemed hours from Technical Resource Manuals (TRM), provide little insight into actual building operations. AEP Ohio overcomes the baseline challenge by requiring a pre-application. This application captures the information needed for engineers to develop a more accurate baseline. Field engineers visit sites before installation to verify the information. Accurate baselines are critical for determining the true kWh saved by a SLS project. For utilities, SLS systems produce actual, not deemed, savings, which lowers risk associated with measurement and verification.

3.3 Low lumen high density projects The key takeaways learned from these projects types are: •

Longer payback period, but still achieve a good deal of savings

Even if only manual switching by facility staff, most office buildings/schools have some sort of automatic control already in place (i.e., time clock on lighting contactor for exterior lighting)

Operate for fewer hours

Each fixture represents a smaller load

Higher incentive levels are needed to drive adoption

Low lumen high density projects are broadly defined as commercial spaces not used for the manufacturing, distribution, or warehousing of a finished product. Common examples include office


spaces, hospitals, and schools. Commercial spaces often employ a range of lighting products from decorative to functional to illuminate a space. These products are often sourced from a variety of manufacturers. The control systems chosen are often hardwired into the structures electrical system. Individual fixtures are tightly spaced with a sensor often controlling multiple fixtures. In buildings with existing building management systems (BMS) the lighting control system can tie into the BMS to increase controllability and functionality. Below are three individual cases from the AEP Ohio SLS program. These cases were chosen because they represent a typical type of space found throughout AEP Ohio’s territory. Large office building The first case is a large class A office building in downtown Columbus, OH. This 280,000+ square foot building is typical of urban office space throughout AEP Ohio’s territory. The structure operates on an 8-hour, 5-day per week schedule. Baselining for this project was determined by a walkthrough of the space and by interviewing maintenance staff. The scope of the project included new LED fixtures and networked lighting controls, qualifying for the full $1.50 per square foot incentive under the third tier of the 2015 program. The cost to retrofit the lighting and install the new control system came to over $1.1 million. AEP Ohio paid $233,247 in incentives, which represents about 21% of the total project cost. The building reduced consumption by 507,986 kWh per year, which correlates to about $45,000 a year in electricity savings. To maximize energy savings, the building’s owners set the maximum fixture output to 70% without incurring complaints from occupants. Beyond energy savings, the building’s owners are pleased that the investment in LED has helped reduce maintenance costs and time needed to switch out lamps. The project took around three months to complete, and the utility incentive was cited as critical to convince decision makers to move the project forward. Small office building The next case is an analysis of a small office space in suburban Ohio. This 2,500-square foot tenant space is typical of suburban office space in AEP Ohio’s territory and operates on an 8-hour, 5-day per week schedule. The scope of this project included an LED retrofit and installation of a networked lighting control system. Baselining for the system was determined by a walkthrough and interviewing maintenance staff. The project, completed in late 2015, qualified for the tier 3 incentive paid at $1.50 per square foot. The cost to the building owner to retrofit the lighting and install the control system came to $11,000. The utility incentive of $3,834 represents about 34% of the total project cost. The retrofit reduced energy consumption by 7,864 kWh per year and reduced the owner’s electric bill by about $640 a year. The project produced non-energy benefits for the tenants as well, improving visual acuity and lowering maintenance costs. The reporting capabilities of the system has helped management understand how the space was being used outside of normal business hours. Public school The final low lumen case analysis is a medium-size public school in rural Ohio. This 47,500-square foot facility is typical for rural schools in AEP Ohio’s territory and operates on a 16-hour, 5-day per week schedule. The scope of this project included an LED retrofit and installation of a networked lighting control system. Baselining was determined by conducting a walkthrough of the facility and interviewing staff. The project was completed in early 2016, and was paid at the $1.50 per square foot incentive based on the 2015 program guidelines. The total project cost $247,000 and utility incentives of $51,055 represent about 21% of the total. The project reduced energy consumption by 89,272 kWh per year, saving the school district about $8,000 annually on electricity. Utility incentives are


necessary for helping school districts adopt emerging technology, such as SLS. Research by the National Research Council has proven a link between student productivity and “green” schools.2

3.4 High lumen low density projects The key takeaways learned from these project types are: •

High bay fixtures operate for longer hours

Buildings have fewer controls in place

Existing fixtures tend to be very inefficient

Large square foot areas with low occupancy levels

Lower incentives are needed because they produce higher savings

High lumen low density projects are broadly defined as industrial spaces used for manufacturing, distributing, or warehousing of a finished product. Common examples include manufacturing facilities, distribution centers, and warehouses. Industrial spaces often employ a singular high bay product sourced from a sole manufacturer to illuminate a space. The control systems chosen are often wireless, which lower installation cost. Individual fixtures are widely spaced with a sensor often integrated into the fixture. Industrial buildings tend to have fewer existing control strategies in place at the time of a lighting retrofit. Below are three individual cases from the AEP Ohio SLS program. These cases were chosen because they represent the typical types of spaces found throughout AEP Ohio’s territory. Large manufacturer The first case analysis is a large industrial products manufacturer located in Ohio. This 1.3 million square foot facility is typical of large manufacturers in Ohio and operates on a 24/7 schedule. The scope of this project included the installation of LED high bays and networked lighting controls. Establishing a baseline was simple, as the facility never shuts down. The project was completed in 2016, and cost $148,750 to install. The project qualified for a $0.45 incentive based on the 2016 program standards. The $32,000 utility incentive represents about 22% of the overall project cost. The project reduced energy consumption by 255,401 kWh, which represents about a $23,000 annual reduction in the customer’s electricity bill. Medium-sized manufacturer The next case analysis is a medium-size plastic production facility in suburban Ohio. The 230,000square foot facility is typical of mid-size manufacturers and operates on a 24/7 schedule. The scope of this project included an LED retrofit and installation of networked lighting controls. A baseline was easily established as the facility is in continuous operation. The project was completed in 2016, and occurred a total cost of $255,500. The project qualified for a $0.45 incentive based on the 2016 program guidelines. The $103,801 utility incentive represents about 41% of the total project cost. The project reduced energy consumption by 694,028 kWh, which represents about a $55,000 annual reduction to the customer’s electric bill.

2

https://www.nap.edu/read/11756/chapter/1#x


Warehouse The final high lumen low density case analysis is a medium-size warehouse in rural Ohio. This 130,000-square foot facility is typical of warehouse space throughout AEP Ohio’s territory and operates on a 24/7 schedule. The scope of this project included an LED retrofit and installation of networked lighting controls. A baseline was easily established because the facility is in continuous operation. The project was completed in 2016 at a total cost of $125,000. The project qualified for a $0.45 incentive based on the 2016 program guidelines and the $60,460 utility incentive represents about 49% of the total project cost. The project reduced energy consumption by 363,735 kWh, which represents about a $29,000 annual savings to the customer’s electric bill.

4. Non-energy benefits of smart lighting solutions Implementation of SLS systems produce secondary benefits outside of energy savings. These secondary benefits are referred to as non-energy benefits (NEB). Unlike energy savings, which are data driven, NEB are harder to quantify. However, these NEB play a critical role in deciding if a networked control system gets implemented. NEB are used by decision makers in performing a cost/benefit analysis to determine the long-term benefits of a SLS system. The NEB created by SLS impact three distinct market actors. 1. Society: Smart lighting solutions have a positive environmental impact by reducing emissions. As an emerging technology, SLS require special skills that necessitates hiring qualified staff. Finally, there is an economic multiplier effect as more capital is outlaid to implement a SLS system. 2. Utility: Utilities and their agents incur lower costs from measuring and verifying data because SLS systems capture, store, and export data at set time intervals. Most SLS systems have native demand response (DR) capacity and can automatically switch to a DR scene during an event. Finally, the data captured from a SLS system can help utilities produce better forecasts. 3. User: Non-energy benefits can be broken down into two classes: commercial and industrial. These classes follow the same logic as breaking down energy consumption by lumen output and fixture density. When interviewing SLS participants from 2016, we found that NEB influenced program participation differently. While some overlap exists, segmenting NEB to each class of customer will speed market adoption of SLS projects.

4.1 Commercial users We broadly define this category as office buildings, educational facilities and all other types of buildings not being used for manufacturing or warehousing a finished good. Commercial users heavily factored the NEB of SLS when performing a cost/benefit analysis and determining payback. Respondents spoke positively about tenant satisfaction, quality of light, comfort, increased productivity, and lower operating costs. The insight gained from these interviews with early adopters show that commercial users of SLS systems value more than just cost savings from energy efficiency. Additionally, most commercial spaces rely on outside specifiers, such as architects and engineers, for advice on adopting a networked lighting controls solution. While specifiers may have a technical understanding of a SLS system, they may lack an understanding of the additional value that NEB can deliver to their clients. An effective outreach strategy should tailor NEB training directly to the


specification community who can reinforce the positive NEB that a SLS system delivers to the end user. These trainings should focus on: •

The relationship between occupant comfort and tenant retention

How best practices in lighting design affect employee productivity

How SLS lowers operating costs.

4.2 Industrial users We broadly define this category as building types used for manufacturing, distribution, or warehousing a finished good. Like commercial users, industrial users also factored in NEB when performing a cost/benefit analysis and financial payback. Respondents spoke positively about lower maintenance costs, better lighting, improved safety, and increased worker productivity. Smart Lighting Solutions produce different NEB dependent on how they are employed. Most industrial users rely on the advice of facility managers when deciding on a networked control system. Facility managers delegate many functions related to lighting and value training that increases their staff’s knowledge of SLS. An effective outreach strategy should be tailored to facility managers by focusing on NEB that: •

Correct operational deficiencies (illuminating dark spaces)

Increase staff operations and maintenance knowledge of the technology

Focus on efficient operations and lower maintenance costs.

When seeking to grow program participation, an effective outreach strategy needs to include a tailored approach to selling NEB. Acknowledging the different needs of diverse segments helps drive adoption. Non-energy benefits have the effect of lowering acquisition costs and focusing the user on lifetime ownership costs. Our experiences with early adopters shows that NEB are critical in helping grow participation in SLS programs.

5. Learning from the data Any size organization can learn from data to make better decisions. AEP Ohio began its Smart Lighting Solutions Pilot offering an incentive of $0.18 per kWh saved that required a full lighting upgrade and networked controls system. When reviewing the final data the first year, AEP Ohio realized that modifying the offering to a tiered structure based on square footage simplified the application process and mitigated risks associated with baselining. When more data became available at the end of 2014, further analysis showed the ineffectiveness of the tiered approach. Analysis of project types showed that industrial spaces rely on high lumen low density fixtures and commercial spaces rely on low lumen high density fixtures. When further data became available at the end of program year 2016, analysis showed that incentive levels were too high, resulting in a reduction of incentive levels. Early adopters are also learning from their data and modifying behaviors to achieve even greater savings. The dynamic nature of data captured by a SLS system presents a building operator with the opportunity to understand how and where energy consumption happens. When a SLS system is commissioned, detailed schedules for space usages are unknown. After a short period of data collection, a building operator can modify the building’s schedule to further reduce consumption based on actual usage. Outreach efforts that focus on training staff in the operations of a networked lighting control system help building operators and facility managers better leverage their data to make more informed decisions.

Data mining and smart lighting solutions - white paper  

Analyzing insights from installed projects to increase customer adoption

Data mining and smart lighting solutions - white paper  

Analyzing insights from installed projects to increase customer adoption