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Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling

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  • Luo, Na
  • Langevin, Jared
  • Chandra-Putra, Handi
  • Lee, Sang Hoon

Abstract

The expansion of commercial building demand response as a demand-side management resource for the electric grid necessitates new decision support resources for customers seeking to assess the benefit–risk tradeoffs of possible strategies for energy flexible building operations. To address this need, we develop surrogate models that predict the impacts of several load flexibility strategies on commercial building electricity demand and indoor temperature, focusing on offices and retail buildings at multiple scales. The surrogate models are fit to a synthetic database generated via whole building simulations, which establish the relationships between the key operational features of a given strategy and potential changes in building demand and temperature across a variety of contexts. The surrogate models are translated to a Bayesian framework to allow straightforward communication of uncertainty and parameter updating given new evidence. We find strong predictive performance across the suite of models, underscoring the usefulness of the approach in guiding decisions about implementing load flexibility strategies under a particular set of operational and environmental conditions.

Suggested Citation

  • Luo, Na & Langevin, Jared & Chandra-Putra, Handi & Lee, Sang Hoon, 2022. "Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling," Applied Energy, Elsevier, vol. 309(C).
    • Handle: RePEc:eee:appene:v:309:y:2022:i:c:s0306261921016147
    DOI: 10.1016/j.apenergy.2021.118372
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    Cited by:

    1. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    2. Anujin Bayasgalan & Yoo Shin Park & Seak Bai Koh & Sung-Yong Son, 2024. "Comprehensive Review of Building Energy Management Models: Grid-Interactive Efficient Building Perspective," Energies, MDPI, vol. 17(19), pages 1-25, September.
    3. Hou, D. & Evins, R., 2024. "A protocol for developing and evaluating neural network-based surrogate models and its application to building energy prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).

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