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Title: High-resolution hourly surrogate modeling framework for physics-based large-scale building stock modeling

Journal Article · · Sustainable Cities and Society
ORCiD logo [1];  [2]; ORCiD logo [1]; ORCiD logo [1];  [1];  [3];  [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [2]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  2. Argonne National Lab. (ANL), Lemont, IL (United States)
  3. Princeton Univ., NJ (United States)
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Surrogate modeling can play a key role in reducing high computational burdens for large-scale physics-based modeling and uncertainty quantification. With the rapid development of large-scale building stock energy modeling, surrogate modeling has also begun to be widely applied in this field; however, most existing surrogate models lack hourly time resolution for regional-scale modeling, which is essential for understanding building demand profiles and grid impacts. Further, there is generally a lack of necessary data and feature engineering frameworks specific to building modeling for efficiently managing large datasets and complex computations. Furthermore, this paper proposes a modeling framework for large-scale (city-/region-scale), high-resolution, high-fidelity surrogate building stock energy models. Our developed framework consists of six modules: (1) building stock energy modeling (ComStockTM and ResStockTM), (2) data engineering for large simulation data, (3) high performance computing workflow, (4) feature engineering, (5) machine learning model development, and (6) model performance evaluation. Two case studies apply the developed framework in both residential and commercial building stock analysis to demonstrate its computational efficiency and surrogate modeling accuracies. Results show that surrogate models, when efficiently trained using the HPC workflow module, reach a high level of modeling accuracy for two case studies.

Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
Grant/Contract Number:
AC36-08GO28308
OSTI ID:
1820582
Report Number(s):
NREL/JA--5500-79567; MainId:35788; UUID:234c9cf2-e4a7-449d-ad40-0952facb3202; MainAdminID:61782
Journal Information:
Sustainable Cities and Society, Journal Name: Sustainable Cities and Society Vol. 75; ISSN 2210-6707
Publisher:
ElseiverCopyright Statement
Country of Publication:
United States
Language:
English

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32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
building stock energy model
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surrogate model