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TOWST: A physics-informed statistical model for building energy consumption with solar gain

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  • Mirfin, Anthony
  • Xiao, Xun
  • Jack, Michael W.

Abstract

Existing data-driven models of building energy consumption have incorporated solar irradiance as a covariate without accounting for key physical constraints, leaving room for improvement in model performance and interpretability. In this paper, we propose the Time-Of-Week, Solar and Temperature (TOWST) model for quantifying building energy consumption. This new model incorporates solar gain into the well-known Time-Of-Week and Temperature (TOWT) model by accounting for important physics constraints, including, building orientation and the opposite impact of solar gain on heating and cooling. An iterative algorithm is developed to cluster the energy demand into heating and cooling categories, identify building orientation, and estimate the unknown model parameters. The model is applied to datasets generated from a building simulation according to international standard ISO 52016-1:2017, for a range of buildings types and locations. Results show a 30%–72% reduction in mean squared error relative to the TOWT model, across a range of different climatic conditions, demonstrating the significance of accounting for solar gain in a physically meaningful way.

Suggested Citation

  • Mirfin, Anthony & Xiao, Xun & Jack, Michael W., 2024. "TOWST: A physics-informed statistical model for building energy consumption with solar gain," Applied Energy, Elsevier, vol. 369(C).
    • Handle: RePEc:eee:appene:v:369:y:2024:i:c:s0306261924008717
    DOI: 10.1016/j.apenergy.2024.123488
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    1. Abanda, F.H. & Byers, L., 2016. "An investigation of the impact of building orientation on energy consumption in a domestic building using emerging BIM (Building Information Modelling)," Energy, Elsevier, vol. 97(C), pages 517-527.
    2. Lü, Xiaoshu & Lu, Tao & Kibert, Charles J. & Viljanen, Martti, 2015. "Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach," Applied Energy, Elsevier, vol. 144(C), pages 261-275.
    3. Granderson, Jessica & Touzani, Samir & Custodio, Claudine & Sohn, Michael D. & Jump, David & Fernandes, Samuel, 2016. "Accuracy of automated measurement and verification (M&V) techniques for energy savings in commercial buildings," Applied Energy, Elsevier, vol. 173(C), pages 296-308.
    4. Walter, Travis & Sohn, Michael D., 2016. "A regression-based approach to estimating retrofit savings using the Building Performance Database," Applied Energy, Elsevier, vol. 179(C), pages 996-1005.
    5. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    6. Díaz, Julián Arco & Ramos, José Sánchez & Delgado, M. Carmen Guerrero & García, David Hidalgo & Montoya, Francisco Gil & Domínguez, Servando Álvarez, 2018. "A daily baseline model based on transfer functions for the verification of energy saving. A case study of the administration room at the Palacio de la Madraza, Granada," Applied Energy, Elsevier, vol. 224(C), pages 538-549.
    7. Granderson, Jessica & Fernandes, Samuel & Touzani, Samir & Lee, Chih-Cheng & Crowe, Eliot & Sheridan, Margaret, 2020. "Spatio-temporal impacts of a utility’s efficiency portfolio on the distribution grid," Energy, Elsevier, vol. 212(C).
    8. Manfren, Massimiliano & Nastasi, Benedetto, 2023. "Interpretable data-driven building load profiles modelling for Measurement and Verification 2.0," Energy, Elsevier, vol. 283(C).
    9. Wei, Yixuan & Xia, Liang & Pan, Song & Wu, Jinshun & Zhang, Xingxing & Han, Mengjie & Zhang, Weiya & Xie, Jingchao & Li, Qingping, 2019. "Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks," Applied Energy, Elsevier, vol. 240(C), pages 276-294.
    10. Li, Qiong & Meng, Qinglin & Cai, Jiejin & Yoshino, Hiroshi & Mochida, Akashi, 2009. "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, Elsevier, vol. 86(10), pages 2249-2256, October.
    11. Skillington, Katie & Crawford, Robert H. & Warren-Myers, Georgia & Davidson, Kathryn, 2022. "A review of existing policy for reducing embodied energy and greenhouse gas emissions of buildings," Energy Policy, Elsevier, vol. 168(C).
    12. Krieger, Elena M. & Casey, Joan A. & Shonkoff, Seth B.C., 2016. "A framework for siting and dispatch of emerging energy resources to realize environmental and health benefits: Case study on peaker power plant displacement," Energy Policy, Elsevier, vol. 96(C), pages 302-313.
    13. Grillone, Benedetto & Mor, Gerard & Danov, Stoyan & Cipriano, Jordi & Sumper, Andreas, 2021. "A data-driven methodology for enhanced measurement and verification of energy efficiency savings in commercial buildings," Applied Energy, Elsevier, vol. 301(C).
    14. Morrissey, J. & Moore, T. & Horne, R.E., 2011. "Affordable passive solar design in a temperate climate: An experiment in residential building orientation," Renewable Energy, Elsevier, vol. 36(2), pages 568-577.
    15. Grillone, Benedetto & Danov, Stoyan & Sumper, Andreas & Cipriano, Jordi & Mor, Gerard, 2020. "A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    16. Tzani, Dimitra & Stavrakas, Vassilis & Santini, Marion & Thomas, Samuel & Rosenow, Jan & Flamos, Alexandros, 2022. "Pioneering a performance-based future for energy efficiency: Lessons learnt from a comparative review analysis of pay-for-performance programmes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
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