High-resolution hourly surrogate modeling framework for physics-based large-scale building stock modeling. (December 2021) - Zhang, Liang; Plathottam, Siby; Reyna, Janet; Merket, Noel; Sayers, Kevin; Yang, Xinshuo; Reynolds, Matthew; Parker, Andrew; Wilson, Eric; Fontanini, Anthony; Roberts, David; Muehleisen, Ralph
Highlights: A surrogate modeling framework is developed for building stock energy models. The surrogate model has hourly time resolution for building load profile studies. Advanced data engineering and HPC workflow provide high computational efficiency. Feature engineering and deep learning techniques are applied in the surrogate model. Two case studies demonstrate the effectiveness of the framework. 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 (ComStock TM and ResStock TM ), (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.