IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i16p5896-d1213770.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/16/5896/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/16/5896/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Grzegorz Dudek, 2022. "A Comprehensive Study of Random Forest for Short-Term Load Forecasting," Energies, MDPI, vol. 15(20), pages 1-19, October.
    2. Arpad Nyers & Jozsef Nyers, 2023. "Enhancing the Energy Efficiency—COP of the Heat Pump Heating System by Energy Optimization and a Case Study," Energies, MDPI, vol. 16(7), pages 1-20, March.
    3. Simone Ferrari & Federica Zagarella & Paola Caputo & Giuliano Dall’O’, 2021. "A GIS-Based Procedure for Estimating the Energy Demand Profiles of Buildings towards Urban Energy Policies," Energies, MDPI, vol. 14(17), pages 1-16, September.
    4. B. V. Surya Vardhan & Mohan Khedkar & Ishan Srivastava & Prajwal Thakre & Neeraj Dhanraj Bokde, 2023. "A Comparative Analysis of Hyperparameter Tuned Stochastic Short Term Load Forecasting for Power System Operator," Energies, MDPI, vol. 16(3), pages 1-21, January.
    5. Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2004. "Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks," Applied Energy, Elsevier, vol. 79(2), pages 159-178, October.
    6. Chen, Jianli & Gao, Xinghua & Hu, Yuqing & Zeng, Zhaoyun & Liu, Yanan, 2019. "A meta-model-based optimization approach for fast and reliable calibration of building energy models," Energy, Elsevier, vol. 188(C).
    7. Anca Mehedintu & Mihaela Sterpu & Georgeta Soava, 2018. "Estimation and Forecasts for the Share of Renewable Energy Consumption in Final Energy Consumption by 2020 in the European Union," Sustainability, MDPI, vol. 10(5), pages 1-22, May.
    8. Colin Cameron, A. & Windmeijer, Frank A. G., 1997. "An R-squared measure of goodness of fit for some common nonlinear regression models," Journal of Econometrics, Elsevier, vol. 77(2), pages 329-342, April.
    9. Tamás Storcz & Zsolt Ercsey & Kristóf Roland Horváth & Zoltán Kovács & Balázs Dávid & István Kistelegdi, 2023. "Energy Design Synthesis: Algorithmic Generation of Building Shape Configurations," Energies, MDPI, vol. 16(5), pages 1-17, February.
    10. Nguyen, Anh-Tuan & Reiter, Sigrid & Rigo, Philippe, 2014. "A review on simulation-based optimization methods applied to building performance analysis," Applied Energy, Elsevier, vol. 113(C), pages 1043-1058.
    11. Alberto Barbaresi & Mattia Ceccarelli & Giulia Menichetti & Daniele Torreggiani & Patrizia Tassinari & Marco Bovo, 2022. "Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need," Energies, MDPI, vol. 15(4), pages 1-16, February.
    12. Ciardiello, Adriana & Rosso, Federica & Dell'Olmo, Jacopo & Ciancio, Virgilio & Ferrero, Marco & Salata, Ferdinando, 2020. "Multi-objective approach to the optimization of shape and envelope in building energy design," Applied Energy, Elsevier, vol. 280(C).
    13. William Mounter & Chris Ogwumike & Huda Dawood & Nashwan Dawood, 2021. "Machine Learning and Data Segmentation for Building Energy Use Prediction—A Comparative Study," Energies, MDPI, vol. 14(18), pages 1-42, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alberto Barbaresi & Mattia Ceccarelli & Giulia Menichetti & Daniele Torreggiani & Patrizia Tassinari & Marco Bovo, 2022. "Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need," Energies, MDPI, vol. 15(4), pages 1-16, February.
    2. Abdo Abdullah Ahmed Gassar & Choongwan Koo & Tae Wan Kim & Seung Hyun Cha, 2021. "Performance Optimization Studies on Heating, Cooling and Lighting Energy Systems of Buildings during the Design Stage: A Review," Sustainability, MDPI, vol. 13(17), pages 1-47, September.
    3. Zhan, Jin & He, Wenjing & Huang, Jianxiang, 2024. "Comfort, carbon emissions, and cost of building envelope and photovoltaic arrangement optimization through a two-stage model," Applied Energy, Elsevier, vol. 356(C).
    4. Huan Zhang & Yajie Wang & Xianze Liu & Fujing Wan & Wandong Zheng, 2024. "Multi-Objective Optimization with Active–Passive Technology Synergy for Rural Residences in Northern China," Energies, MDPI, vol. 17(7), pages 1-25, March.
    5. Rudai Shan & Lars Junghans, 2023. "Multi-Objective Optimization for High-Performance Building Facade Design: A Systematic Literature Review," Sustainability, MDPI, vol. 15(21), pages 1-33, November.
    6. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    7. Shu-Long Luo & Xing Shi & Feng Yang, 2024. "A Review of Data-Driven Methods in Building Retrofit and Performance Optimization: From the Perspective of Carbon Emission Reductions," Energies, MDPI, vol. 17(18), pages 1-33, September.
    8. Tamás Storcz & Zsolt Ercsey & Kristóf Roland Horváth & Zoltán Kovács & Balázs Dávid & István Kistelegdi, 2023. "Energy Design Synthesis: Algorithmic Generation of Building Shape Configurations," Energies, MDPI, vol. 16(5), pages 1-17, February.
    9. Yizhe Xu & Chengchu Yan & Hao Qian & Liang Sun & Gang Wang & Yanlong Jiang, 2021. "A Novel Optimization Method for Conventional Primary and Secondary School Classrooms in Southern China Considering Energy Demand, Thermal Comfort and Daylighting," Sustainability, MDPI, vol. 13(23), pages 1-19, November.
    10. Simone Giostra & Ayush Kamalia & Gabriele Masera, 2024. "Solar Species: Energy Optimization of Urban Form Through an Evolutionary Design Process," Sustainability, MDPI, vol. 16(21), pages 1-27, October.
    11. Benedek Kiss & Jose Dinis Silvestre & Rita Andrade Santos & Zsuzsa Szalay, 2021. "Environmental and Economic Optimisation of Buildings in Portugal and Hungary," Sustainability, MDPI, vol. 13(24), pages 1-19, December.
    12. Ghazi Falah & Michael Hoy & Rakhal Sarker, 2000. "Co-existence in Selected Mixed Arab-Jewish Cities in Israel: By Choice or by Default?," Urban Studies, Urban Studies Journal Limited, vol. 37(4), pages 775-796, April.
    13. Cowell, Frank & Flachaire, Emmanuel & Bandyopadhyay, Sanghamitra, 2009. "Goodness-of-fit: an economic approach," LSE Research Online Documents on Economics 25433, London School of Economics and Political Science, LSE Library.
    14. Al-Ghandoor, A. & Jaber, J.O. & Al-Hinti, I. & Mansour, I.M., 2009. "Residential past and future energy consumption: Potential savings and environmental impact," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1262-1274, August.
    15. Guariso, Giorgio & Sangiorgio, Matteo, 2019. "Multi-objective planning of building stock renovation," Energy Policy, Elsevier, vol. 130(C), pages 101-110.
    16. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    17. Waibel, Christoph & Evins, Ralph & Carmeliet, Jan, 2019. "Co-simulation and optimization of building geometry and multi-energy systems: Interdependencies in energy supply, energy demand and solar potentials," Applied Energy, Elsevier, vol. 242(C), pages 1661-1682.
    18. Kokaraki, Nikoleta & Hopfe, Christina J. & Robinson, Elaine & Nikolaidou, Elli, 2019. "Testing the reliability of deterministic multi-criteria decision-making methods using building performance simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 991-1007.
    19. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
    20. Li, Jiarong & Li, Xiangdong & Wang, Yong & Tu, Jiyuan, 2020. "A theoretical model of natural circulation flow and heat transfer within horizontal evacuated tube considering the secondary flow," Renewable Energy, Elsevier, vol. 147(P1), pages 630-638.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:5896-:d:1213770. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.