A neural network-based surrogate model to predict building features from heating and cooling load signatures

ABSTRACT

Addressing the challenges of scalable and cost-effective energy performance analysis in mid to high-rise office buildings, this paper introduces a novel approach utilizing an inverse-based artificial neural network (ANN). This ANN was trained on synthetically generated heating and cooling load parameters – derived from simulations conducted in EnergyPlus – to predict characterization parameters, including the building envelope, internal heat gains, and HVAC operational parameters. Diverging from traditional forward surrogate models, this inverse surrogate model fills a critical gap in current building energy modeling approaches that are hindered by data and resource limitations. Its effectiveness is verified with a testing dataset of 3000 buildings and is further demonstrated through a case study in Ottawa, Ontario. Proving to be an efficient, cost-effective tool for energy retrofit screening, the model is enhanced by a user-friendly web-based application (Ferreira and Gunay), marking a significant advancement in accessible building energy analysis.

KEYWORDS:

• Surrogate model
• inverse model
• artificial neural network
• remote building characterization
• retrofit decision-making
• ensemble learning

Data availability

The data and code supporting the findings of this study are available at:

• Zenodo: [Building Feature Characterization Tool] ( https://doi.org/10.5281/zenodo.11581431

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  )

• GitHub: [Building Feature Characterization Tool] (https://github.com/Carleton-DBOM-Research-Group/building-feature-characterization-tool)

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by National Research Council Canada: [Grant Number 1012783].