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2017 Winter Conference
Tianzhen Hong, PhD, PEBuilding Technology and Urban Systems Division
Lawrence Berkeley National [email protected]
A Data and Computing Platform for City and District Scale Building Energy Efficiency
Las Vegas, Nevada
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Learning Objectives• Learn how urban-scale building modeling can be used to make more
well-informed energy decisions• Learn challenges of big data and computing for city scale building
energy modeling• Understand why metropolitan planning agencies are starting to become
more interested in building energy use• Understand the interactions between data collected in the SEED
Platform through energy disclosure laws and city scale modeling with DECAF
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.
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Acknowledgements
• Berkeley Lab’s Laboratory Directed Research and Development (LDRD) Program
• Department of Energy: Building Technologies Office, Office of Science
• City of San Francisco• Team members: Yixing Chen, Mary Ann Piette
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Outline/Agenda
• Introduction• The Platform • Case Study• Challenges• Conclusions
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Cities drive our economy and dominate energy and environmental challenges.
Why Cities?
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• Consume 30-70% of total primary energy in cities• Need deep retrofit and scale up• Provide opportunity for integration
Why City Buildings?
City Energy Profiles
San DiegoDenver
PortlandSacramento
SeattleSan
FranciscoSt. Louis
MinneapolisChicagoBoston
New YorkWashingtonPhiladelphia
Baltimore
Comm./Ind. TransportResidential 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
GOAL: 50% energy reduction in city building stock
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City Building Energy Saver (CityBES)
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1. An open-access web-based platform to support city and district scale building energy efficiency programs.
2. Creation and evaluation of energy retrofit scenarios for city buildings.
3. Visualization (3D + GIS) of existing buildings’ performance data.
4. Detailed energy modeling and simulation, considering inter-building effect and interactions with urban climate
5. Builds upon open standards
Overview of the Platform
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Software Architecture
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CityGML• International OGC standard
for representation and exchange of 3D city models.
• Started in 2002, v.2.0 in 2012• Multi-resolution model• Customization and
extensibility
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Open City Buildings Datasets
Translate diverse city datasets into inter-operable, standardized format
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An Exascale Computing Problem
106 bldgs1013 FLOP/bldg20 interconnection50 scenarios
+ 3 hours run-time= 1018 FLOPS
Exascale computing project: Multiscale Coupled Urban Systems
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Case Study – Retrofit Analysis• 540 small/medium-sized commercial buildings in Downtown San Francisco• 5 common retrofits• Together save 22-48% of site energy per building• LED upgrades, air economizer additions are most cost effective• Long payback for HVAC upgrade in mild climate
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Visualizing Performance of City Buildings
Visualizing the San Francisco energy ordinance dataset with 1,573 buildings: (1) filtering buildings by type,size, vintage, and (2) color-coding by EUI, CO2 emission, ENERGY STAR score, compliance status.
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Challenges
1. DataA big data problem integrating diverse sources with different temporal and spatial resolutions, quality, and structure/format.
2. ModelingIntegration of multiple domain models with different scales and resolutions.
3. SimulationAn exascale computing problem.
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Conclusions
• An open data and computing platform enables cities to make data- and model- driven decisions on building energy efficiency at scale.
• Challenges to address: data, modeling and simulation.
• Collaborating with cities and their stakeholders is key.
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Bibliography
• T. Hong, Y. Chen, S. Lee, and M.A. Piette, “CityBES: A Web-based Platform to Support City-Scale Building Energy Efficiency”, The 5th International Workshop on Urban Computing, August 14, 2016, San Francisco
• T. Hong, M.A. Piette, Y. Chen, et al. Commercial Building Energy Saver: An energy retrofit analysis toolkit, Applied Energy, 159: 298-309, 2015.
