TY - JOUR
T1 - Modularized neural network incorporating physical priors for future building energy modeling
AU - Jiang, Zixin
AU - Dong, Bing
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/8/9
Y1 - 2024/8/9
N2 - Building energy modeling (BEM) is fundamental for achieving optimized energy control, resilient retrofit designs, and sustainable urbanization to mitigate climate change. However, traditional BEM requires detailed building information, expert knowledge, substantial modeling efforts, and customized case-by-case calibrations. This process must be repeated for every building, thereby limiting its scalability. To address these limitations, we developed a modularized neural network incorporating physical priors (ModNN), which is improved by its model structure incorporating heat balance equations, physically consistent model constraints, and data-driven modular design that can allow for multiple-building applications through model sharing and inheritance. We demonstrated its scalability in four cases: load prediction, indoor environment modeling, building retrofitting, and energy optimization. This approach provides guidance for future BEM by incorporating physical priors into data-driven models without extensive modeling efforts, paving the way for large-scale BEM, energy management, retrofit designs, and buildings-to-grid integration.
AB - Building energy modeling (BEM) is fundamental for achieving optimized energy control, resilient retrofit designs, and sustainable urbanization to mitigate climate change. However, traditional BEM requires detailed building information, expert knowledge, substantial modeling efforts, and customized case-by-case calibrations. This process must be repeated for every building, thereby limiting its scalability. To address these limitations, we developed a modularized neural network incorporating physical priors (ModNN), which is improved by its model structure incorporating heat balance equations, physically consistent model constraints, and data-driven modular design that can allow for multiple-building applications through model sharing and inheritance. We demonstrated its scalability in four cases: load prediction, indoor environment modeling, building retrofitting, and energy optimization. This approach provides guidance for future BEM by incorporating physical priors into data-driven models without extensive modeling efforts, paving the way for large-scale BEM, energy management, retrofit designs, and buildings-to-grid integration.
KW - building energy modeling
KW - building retrofit
KW - data-driven
KW - energy flexibility
KW - load prediction
KW - model predictive control
KW - modularized neural network
KW - physics-inspired neural network
UR - http://www.scopus.com/inward/record.url?scp=85207374881&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85207374881&partnerID=8YFLogxK
U2 - 10.1016/j.patter.2024.101029
DO - 10.1016/j.patter.2024.101029
M3 - Article
AN - SCOPUS:85207374881
SN - 2666-3899
VL - 5
JO - Patterns
JF - Patterns
IS - 8
M1 - 101029
ER -