Urban energy design software development: Aiko Nakano (MIT), Bruno Bueno (Fraunhofer Institute for Solar Energy), Leslie Norford (MIT), Christoph Reinhart (MIT)
URBAN HEAT ISLAND EFFECT MODELING IN ENERGY SIMULATIONS
Seletar Farmway [T reference site]
Punggol Field
Urban Weather Generator [UWG] estimates the hourly urban canopy air temperature and humidity using weather data from simulation programs. The model is based on energy conservation principles and is a bottom-up building stock model.
Pasir Ris Dr
It has been tested for Basel, Switzerland; Toulouse, France[1]; and several neighborhoods in Singapore, shown here. Calculated urban air temperatures are compared with measurements from a network of weather stations in Singapore, representing a range of land uses, morphological parameters and building usages. The simulated results are accurate within the range of air temperature variability observed in different locations within the same urban area for all three tested cities.
Tampines St
Changi Airport
The performance of UWG is comparable to a more computationally expensive mesoscale atmospheric model. The model shows satisfactory performance for all weather conditions and for different reference sites, which validates UWG’s robustness and suitability as an urban simulation engine.
Limau Grove
Bideford Rd
model validation through singapore data [4]
Penang Rd urban boundary layer
urban boundary layer model: energy balance for the control volume calculates air T above urban canopy layer
vertical diffusion model: of air T above the weather station
rural sensible heat flux
Kim Cheng Rd
average diurnal cycle of canopy layer air temperatures calculated by UWG and observed at urban sites
radiation
urban sensible heat flux
2km
neighborhood boundary
Tsky
advection
The model overpredicts February daytime and underpredicts July nighttime air T.
advection canopy exchange
roof window
Trural
rural boundary layer
Tsky
Tsky
Tsky
simulated measured Seletar
wall
mass
road
road
urban canopy layer
reference weather station
28
urban canopy + building energy model: Town Energy Balance [2] scheme including its building energy model [3]
RMSE 0.9K MBE 0.5K
24 4:00
METEOROLOGICAL PARAMETERS daytime + nighttime boundary layer height
simulation parameters
July 2010 [SW monsoon period]
32
Tsky
rural station model: energy balance at the soil surface calculates
February 2010 [NE monsoon period]
obs Seletar
T [ C]
12:00
RMSE 1.2K MBE -0.5K
20:00
4:00
12:00
20:00
URBAN GEOMETRY PARAMETERS H
assumed uniform
=
uwg’s robustness for different reference stations
bldg
Asite
vertical to horizontal built ratio VH =
wtd
Asite
max
urban canyon URBAN MORPHOLOGY PARAMETERS
building perimeter P building footprint Abldg
internal heat gains [heat flux from AC is calculated by the model] building construction + road surface material properties
and is relative to the chosen reference site. Despite this, UWG is able to produce similar urban temperatures. This robustness is pertinent to the design tool as data may not be available for a site that captures climate conditions upwind of the city [i.e. Seletar Farmway data is not public]
weighted average building height [by footprint] hwtd
vegetation [trees + grass; trees are treated as shading devices for urban canyon] REFERENCE SITE PARAMETERS road surface material properties vegetation site area Asite
Seletar Farmway
Feb Jul
RMSE [K] 0.9 1.2
MBE [K] 0.5 (0.5)
Tmax [K] 1.8 2.5
Changi Airport
Feb Jul
0.9 0.8
0.6 (0.2)
1.2 1.0
POLICIES AND REGULATIONS ARTICULATED WITH MICROCLIMATIC DESIGN
model
[K] 0.2 0.2
measured [K]
0.2 0.2
0.3 0.3 0.3 0.3
software workflow 9.71
9.50
The software can estimate the effects of urban morphology, geometry, and surface materials on temperature and energy consumption. This enables urban designers to parametrically test built densities and vegetation for masterplanning. Urban
5.76 12.54
12.76
6.46
11.36
1.25 12.47
12.14
cool roof with energy and thermal implications of these interventions. An optimization tool is currently being developed to
14.90
1.48
1.20
2.59
2.65
16.73 1.25
1.19
2.83
2.87 2.67 1.31
1.11
As UWG requires over 50 parameters, sensitivity analyses are performed to identify the most important parameters and reduce the number of user inputs. An earlier study for Toulouse and Basel [mild climates] has shown that vertical to horizontal built ratio, horizontal building density, and vegetation are most sensitive. Additional study for Singapore [tropical] and Boston, MA [cold, shown below] are conducted to determine the most effective design strategies for each climate. key urban design and planning strategies to reduce energy use and thermal discomfort horizontal building density
vertical to horizontal built ratio
15
sensible anthropogenic heat
2.76 2.43
2.55 3.67 1.65
4.15 1.21 2.65
1.87
7.23
1.29
2.68 1.34
1.66
1.27
2.70
2.45
2.75 1.50
1.39 1.35
1.92 1.23
model a city with Rhino or obtain GIS model for existing cities
1.31 1.33
1.24
Extract UWG model parameters and
Run UWG to capture UHI and then energy simulation using umi
A number of simulation tools have been developed at MIT. The presented software will be available as a stand-alone tool and a plug-in for umi to expand its capabilities to parametrically
13 T [ C]
T [ C]
13
2.69
3.44
integral component of family of design tools
albedo + emissivity: green + cool roof
15
5.32
11
11 Thermal Diversion |
9 0:00
6:00
12:00
|
18:00
0:00
6:00
12:00
18:00
|
| 0:00
6:00
12:00
9 0:00
18:00
3.4
6:00
12:00
18:00
9.0
3.0 1
2
3
3.7 1
4.9 2
Low
Average
sensitivity analysis thermal and energy metrics
6.1 3
67.8 2
25.0 1
8.5
135.5 3
a
0.05 1 0.98
0.20 2 0.90
Green Roof
Concrete
Urban Modeling Interface
Archsim Energy Modeling
DIVA
urban-scale tool for energy, thermal comfort, etc for Rhino
Energy modeling plug-in for Grasshopper
Daylighting and energy simulation plug-in for Rhino
0.95 3 0.89 Cool Roof
#hr
1,000
References:
500
Journal of Building Performance Simulation 1-13. [2] V. Masson. A physically-based scheme for the urban energy budget in atmospheric models. Boundary-Layer Meteorology 94 (2000) 357-397.
--
0.1
0
.50
0
.90
1
.30
1
.70
2
.10
.50
2
2
.90
3
.30
Boston Financial District
UWG and E+ hourly results are compared against ‘base’ urban scenario which has model parameters extracted from GIS and
Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme.
[3] Bueno, B., et al. (2012). Development and evaluation of a building energy model
canopy level urban air temperature at the neighborhood scale. Accepted for Urban Climate.
can be downloaded from http://urbanmicroclimate.scripts.mit.edu