High-Resolution Hourly Surrogate Modeling Framework for Physics-Based Large-Scale Building Stock Modeling

Liang Zhang, Siby Platthotam, Janet Reyna, Noel Merket, Kevin Sayers, Xinshuo Yang, Matthew Reynolds, Andrew Parker, Eric Wilson, Anthony Fontanini, David Roberts, Ralph Muehleisen

Research output: Contribution to journalArticlepeer-review

11 Scopus Citations

Abstract

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. 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.

Original languageAmerican English
Article numberArticle No. 103292
Number of pages18
JournalSustainable Cities and Society
Volume75
DOIs
StatePublished - Dec 2021

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Ltd

NREL Publication Number

  • NREL/JA-5500-79567

Keywords

  • Building stock energy model
  • Data engineering
  • Electric load profiles
  • High-performance computing
  • Machine learning
  • Surrogate model

Fingerprint

Dive into the research topics of 'High-Resolution Hourly Surrogate Modeling Framework for Physics-Based Large-Scale Building Stock Modeling'. Together they form a unique fingerprint.

Cite this