Embed Size (px)
Citation preview
Dr Yeonsook Heo
Department of Architecture, University of Cambridge UK Energy Efficient Cities initiative
Urban-Scale Energy Analyses of the Built Environment
Seminar 39: Data Sources toward Urban-Scale Energy Modeling, Part 1
Learning objectives for this session
• Provide an overview of Urban Building Energy Model approaches and data sources.
• Describe the tradeoffs between speed and accuracy when using calibrated archetypes.
• Understand the advantages and limitations of building archetype calibration
• Describe how urban-scale building modeling can be used to make more well-informed energy decisions.
Disclaimer: ASHRAE is a Registered Provider with The American Institute of Architects Continuing Education Systems. Credit earned on completion of this program will be reported to ASHRAE Records for AIA members. Certificates of Completion for non-AIA members are available on request. This program is registered with the AIA/ASHRAE for continuing professional education. As such, it does not include content that may be deemed or construed to be an approval or endorsement by the AIA of any material of construction or any method or manner of handling, using, distributing, or dealing in any material or product. Questions related to specific materials, methods, and services will be addressed at the conclusion of this presentation.
Why do we need urban-scale energy analyses?
Potential Benefits….
• Inform impact of urban measures. For example, projected growth in buildings; roll-out of new technologies; new planning laws; more stringent regulations.
• Assess relationships with exogenous influences on energy and environment: such as spatial planning, transport systems, industries, policies, etc.
• Enable synergistic planning of energy supply and transport networks in support of energy reduction and improved air quality.
City-scale analysis of buildings
City-scale Analysis of Buildings
Rationale for pursuing an “dynamic” energy simulation of “each building” in a building stock:
• Improved profiling of peak load duration curves (inferring specific causes of peaks and troughs in energy demand)
• Improved ability to cost energy services dependent on peak demand and time-of-use
• Improved representation of building physics (thus improved knowledge of the physical interaction between energy supply technologies, building physics)
Overview of data sources used for urban-scale energy analysis
EECi inventory
of building
simulation data
and models
GIS UKMap of Westminster
Westminster EPC Register
UK Public-sector DECs
Research literature
Government reports
US DoE
Open-source models
Why is Westminster chosen for urban-scale modelling?
Why is Westminster chosen for urban-scale modelling?
Non-domestic landuse area
Why is Westminster chosen for urban-scale modelling?
Extraction of physical geometry
GIS UKMap Data
Summary
• Spatial map of Westminster buildings, differentiated by type
• Buildings represented as polygons
• Multiple polygons can represent one building
• Building heights provided
Extraction of physical geometry
Spot Check 1: Polygon G9527333 - Office (NE Corner of Regent St. and Great Marlborough St.)
• This polygon represents the top three floors of the corner suite (14.5 m in height). It is an office situated above a Banana Republic retail outlet
Google StreetView
GIS Data Generated IDF (Wei’s script)
Spot Check 2: Polygon G9532901 - Office (NW Corner of St James Place, Alongside Hyde Park)
• This is a complete building with partial exposure on side facades, and full exposure on front and rear facades.
Google StreetView
GIS Data Generated IDF (Wei’s script)
Spot Check 3: Polygon G9533283 – Office (W Corner of Cleveland Row, North Side)
• This is a complete building with full exposure. Notice that there are more modelled levels than in reality. This is a floor heights issue, as the total height of the building seems to be encapsulated by the Eplus model
Google StreetView
GIS Data Generated IDF (Wei’s script)
Thermodynamic properties of buildings
Westminster EPC Register
Summary (Domestic sector)
• Coverage for ~64,000 dwellings
• Includes building construction information (wall type, window type, etc.)
• Includes heating system type
Summary (Non-domestic sector)
• Coverage for ~8,500 premises
• Does not include construction information
• However, includes retrofit recommendations per premises (e.g., “replace glazing”)
Thermodynamic properties of buildings
Research literature
Reference Smith, S. Modelling thermal loads for a non-domestic building stock. Associating a priori probability with building form and construction - using building control laws and regulations. PhD Thesis, University of Nottingham, 2009
Summary
• Provided statistical correlations building construction properties with respect to building construction age
• Suggest values for pre 19th-century buildings up to present
Thermodynamic properties of buildings
Government Reports and Data
References
• City of London Data store • Defra (2007-2010): Market Transformation Program (MTP) • Element Energy (2009): Uptake of energy efficiency measures • CIBSE Guide A • Etc. Summary
• Provided distribution of construction age of non-domestic building stock in Westminster borough (by type)
• Provided distribution of energy efficiency measures and energy supply systems amongst UK non-domestic buildings (present and future scenarios)
Distribution of non-domestic building construction ages, Westminster.
Review of baseline data for Westminster
Schedules of building use and operation
US Department of Energy
References
• DoE Commercial / Residential Reference Buildings
Summary
• Simple, deterministic schedules for occupancy and internal gains based on surveys of randomly selected US buildings
• Differentiated by type of building
Generic modeling process for buildings summarized
Assign building construction properties
Determine building type
Select building polygon
Generate 3D building model
Simulate building
UKmap
UKmap
EPCs, Literature, Government data, etc.
EECi models
EnergyPlus
Process for assigning building construction information (non-domestic Bldgs)
Determine building subtype (if
retail)
Sample asset rating and AC
status from EPC/DEC
distribution of all buildings
in subtypes
Sample asset rating and AC
status from nearest
neighbor(s)
Does EPC exist?
Determine building
construction likelihood from
DEC recommendations of
chosen neighbor
Determine building
construction likelihood from
exogenous data
Determine building age
Determine building age
Start
Yes
No
Source: GIS data provides building type
Source: City of London statistical data on building age by building type in Westminster
Source: Statistical data on breakdown of retail floor area by subtype (e.g., restaurants, food retail, non-food retail, etc.)
Source: Academic publications, providing statistical data on building construction properties
Source: EPC/DEC register for London and City of Westminster
Constructing the simulation (and the data challenge) for ~100,000 buildings
UKmap
EPCs
Other data
100 MB
80 MB
~1 MB
Geometries
as text
Sampled building
construction
properties
19 MB
20 MB
EnergyPlus 3D
models
6.6 GB
Raw data Pre-processed data
Processed simulation inputs
Constructing the Simulation (and data challenge)
EnergyPlus 3D
models
6.6 GB
Processed simulation inputs
Raw simulation
output files
Simulation outputs
~600-800 GB (roughly 5-8 MB per building
Answer
• Cambridge HPC Darwin computing cluster
• 10x16 processor cores used (2.6 GHz Intel Sandybridge cores)
• Simulations conducted within ~4 hours
Constructing the Simulation (and data challenge)
Raw simulation
output files
Simulation outputs
~600-800 GB (roughly 5-8 MB per building
Outputs:
• Annual load duration curves (heating, cooling, electricity)
• Hourly end-use energy demand, on average days (3) per month
• Monthly end-use energy demand
• Annual end-use energy demand
Processed
simulation data
~16 GB
Processed simulation outputs
Constructing the Simulation (and data challenge)
Outputs:
• Annual primary (gas and elec.) energy consumption per building
• Annual CO2 attributable emissions
• NO2 and PM2 emissions at source
Processed
simulation data
~16 GB
Processed simulation outputs
Final dataset
~16 GB
Calculation of primary energy and emissions
Westminster Annual Heat Intensity
Westminster Annual Heat Intensity
Annual Gas Consumption – Non-domestic Sector: DECC MLSOA 2011
Validating the city-scale model against measured energy use data
Measured energy use data
• Annual gas/electricity consumption
• Domestic sector: spatial aggregation at LLSOA (lower layer super output area)
• Non-domestic sector: spatial aggregation at MLSOA (middle layer super output area)
Annual Gas Consumption – Domestic Sector: DECC LLSOA 2011
Actual Annual Gas Consumption – Non-domestic Sector: DECC MLSOA 2011
Modelled Annual gas consumption, non-domestic sector
Comparing predicted gas energy consumption at MLSOA level vs actual
Actual Annual Gas Consumption – Domestic Sector: DECC LLSOA 2011
Modelled Annual gas consumption, Domestic sector
Comparing predicted gas energy consumption at LLSOA level vs actual
The Good
So far, what have we achieved?
• Confident with allocation of building physical properties across Westminster stock
• 3D building geometry quality consistent with literature
• Spot checks yield “reasonable” results with respect to available benchmarks
• Initial results near MLSOA and LLSOA metered data
The Bad
• Less information on the variance of indoor equipment/lighting use
• Deterministic representation of occupancy-driven profiles yields over-amplified peaks and troughs in demand
Importance of representing variance in building use and operation
Building Use & Operation Minimum Maximum Base
Heating setpoint T – occupied (°C) 19.5 22.5 21.0
Heating setpoint T – unoccupied (°C) 15.0 18.0 15.6
Cooling setpoint T – occupied (°C) 22.5 25.5 24.0
Cooling setpoint T – unoccupied (°C) 25.0 28.0 30.0
Occupant density (m2/person) 4.3 22.8 9.0
Lighting power density (W/m2) 6.2 33.9 11.0
Equipment power density (W/m2) 5.7 34.0 11.0
Plausible range of parameter values for office buildings
- We have assumed uncertain parameters to have triangular distributions - We have used Latin Hypercube Sampling to draw samples from the
distributions - For the following analyses, a representational sample of 11 office
buildings was used
Importance of representing variance in building use and operation
Plausible range of parameter values for office buildings
0
50
100
150
200
250
300
350
400
EU
I (k
Wh
/m
2)
Westminster Office Buildings
Base EUI
Mean EUI
Importance of representing variance in building use and operation
Sensitivity analysis of building use and operation parameters (using standardised regression coefficients)
Operational Parameters Electricity Heating
Occupant density - 0.17
Lighting power density 0.44 - 0.35
Equipment power density 0.90 - 0.56
Heating T- occupied - 0.66
Heating T- unoccupied - 0.20
Cooling T- occupied - - 0.02
Cooling T- unoccupied - -
Evaluating the effect of modelling assumptions on prediction
Office Building
No. Floor
Floor Area
WWR
Daylight Control
Window U-value
Infiltration
A 6 78 0.21 1 4.0 0.6
B 5 350 0.16 0 4.6 0.5
C 6 1098 0.19 1 5.3 0.4
D 2 34 0.04 1 5.0 0.8
E 6 156 0.15 1 1.6 0.8
F 3 108 0.10 1 5.7 0.6
G 6 1026 0.09 1 5.4 0.8
H 10 1530 0.09 1 2.8 0.3
I 7 337 0.10 1 5.2 0.7
J 5 230 0.07 1 4.9 0.5
K 5 1025 0.22 1 1.4 0.7
L 11 308 0.24 0 5.2 0.5
M 6 2802 0.21 0 4.5 0.4
N 7 119 0.23 1 5.1 0.6
O 5 390 0.37 1 5.4 0.6
P 4 48 0.17 1 4.1 0.6
Q 5 600 0.03 1 5.1 0.7
R 8 312 0.10 1 5.0 0.6
S 8 552 0.12 0 5.4 0.6
T 3 369 0.32 1 6.1 0.5
Evaluating the effect of modelling assumptions on prediction
Equipment power density
T_heat_occ
Lighting power density
T_heat_unocc
Geometry + thermal properties
Case 1 Indiv. Indiv. Indiv. Indiv. Indiv.
Case 2 Base Indiv. Indiv. Indiv. Indiv.
Case 3 Base Base Indiv. Indiv. Indiv.
Case 4 Base Base Base Indiv. Indiv.
Case 5 Base Base Base Base Indiv.
Case 6 Base Base Base Base Base
Annual gas consumption
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1 2 3 4 5 6
CV
RM
SE
Case
Buildings analysis conclusions
• Modelling individual buildings is important to correctly capture variability in the energy performance of buildings
• Our project developed the semi-automated process for generating the energy simulation model of every building in the portfolio
Further work
• Incorporation of variability in building use and operation into the model
• Deeper validation analysis between individual modelled buildings and DEC-covered premises
Adequacy of urban-scale modelling
