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Regression Models and Shape Descriptors for Building Energy Demand and Comfort Estimation

Author

Listed:
  • Tamás Storcz

    (Department of Systems and Software Technologies, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány Street 2, H-7624 Pécs, Hungary
    Autonomous Technologies and Drones Research Team, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány Street 2, H-7624 Pécs, Hungary)

  • Géza Várady

    (Autonomous Technologies and Drones Research Team, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány Street 2, H-7624 Pécs, Hungary
    Department of Technical Informatics, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány Street 2, H-7624 Pécs, Hungary)

  • István Kistelegdi

    (Department of Energy Design, Ybl Miklós Faculty of Architecture and Civil Engineering, Óbuda University, Thököly út 74, H-1146 Budapest, Hungary)

  • Zsolt Ercsey

    (Department of Systems and Software Technologies, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány Street 2, H-7624 Pécs, Hungary
    Autonomous Technologies and Drones Research Team, Faculty of Engineering and Information Technology, University of Pécs, Boszorkány Street 2, H-7624 Pécs, Hungary)

Abstract

Optimal building design in terms of comfort and energy performance means designing and constructing a building that requires the minimum energy demand under the given conditions while also providing a good level of human comfort. This paper focuses on replacing the complex energy and comfort simulation procedure with fast regression model-based processes that encounter the building shape as input. Numerous building shape descriptors were applied as inputs to several regression models. After evaluating the results, it can be stated that, with careful selection of building geometry describing design input variables, complex energy and comfort simulations can be approximated. Six different models with five different building shape descriptors were tested. The worst results were around R 2 = 0.75, and the generic results were around R 2 = 0.92. The most accurate prediction models, with the highest level of accuracy (R 2 > 0.97), were linear regressions using 3rd power and dense neural networks using 1st power of inputs; furthermore, averages of mean absolute percentage errors are 1% in the case of dense neural networks. For the best performance, the building configuration was described by a discrete functional point cloud. The proposed method can effectively aid future building energy and comfort optimization processes.

Suggested Citation

  • Tamás Storcz & Géza Várady & István Kistelegdi & Zsolt Ercsey, 2023. "Regression Models and Shape Descriptors for Building Energy Demand and Comfort Estimation," Energies, MDPI, vol. 16(16), pages 1-20, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:5896-:d:1213770
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    References listed on IDEAS

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