Modularized neural network incorporating physical priors for future building energy modeling

Zixin Jiang, Bing Dong

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number101029
JournalPatterns
Volume5
Issue number8
DOIs
StatePublished - Aug 9 2024

Keywords

  • building energy modeling
  • building retrofit
  • data-driven
  • energy flexibility
  • load prediction
  • model predictive control
  • modularized neural network
  • physics-inspired neural network

ASJC Scopus subject areas

  • General Decision Sciences

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