IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v377y2025ipcs0306261924020038.html
   My bibliography  Save this article

A hybrid load prediction method of office buildings based on physical simulation database and LightGBM algorithm

Author

Listed:
  • Lian, Huihui
  • Ji, Ying
  • Niu, Menghan
  • Gu, Jiefan
  • Xie, Jingchao
  • Liu, Jiaping

Abstract

Building load prediction plays an important role in building energy savings and mechanical and electrical system optimization control. The dynamic energy consumption can be accurately calculated using the traditional physical energy simulation method that entails a complex setup and verification process due to the numerous input parameters required. It is also difficult to change the physical model once it has been determined. The data-mining method is fast in calculation and simple to use, but its prediction accuracy is limited by historical data quality. It is difficult to predict the load of new buildings without historical data. To solve these problems, this study proposes a hybrid building load prediction method for office buildings. The proposed method uses EnergyPlus to generate a building load database that includes than 25.14 million data cases, covering 35 types of building geometry and 2870 building examples. Based on the above database, the LightGBM algorithm was selected to extract feature variables that affect the load and build a load prediction model. The training results show that there are 24 key feature variables for office building load prediction. The hourly MAPE of the cooling load prediction model is 6.95 % and RMSE is 4.31 W/m2, and the hourly MAPE of the heating load prediction model is 7.09 % and RMSE is 11.64 W/m2 compared with EnergyPlus model. Two actual office buildings are selected as case studies to validate the model prediction accuracy. Results show that comparing predicted results with measured data, the hourly cooling load of the MAPE is 12.42 %. Coparing predicted results with actual heating load, daily MAPE is 7.97 %.

Suggested Citation

  • Lian, Huihui & Ji, Ying & Niu, Menghan & Gu, Jiefan & Xie, Jingchao & Liu, Jiaping, 2025. "A hybrid load prediction method of office buildings based on physical simulation database and LightGBM algorithm," Applied Energy, Elsevier, vol. 377(PC).
    • Handle: RePEc:eee:appene:v:377:y:2025:i:pc:s0306261924020038
    DOI: 10.1016/j.apenergy.2024.124620
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924020038
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.124620?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wang, P.P. & Huang, G.H. & Li, Y.P. & Liu, Y.Y. & Li, Y.F., 2024. "An ecological input-output CGE model for unveiling CO2 emission metabolism under China's dual carbon goals," Applied Energy, Elsevier, vol. 365(C).
    2. Wang, Zhe & Hong, Tianzhen & Piette, Mary Ann, 2020. "Building thermal load prediction through shallow machine learning and deep learning," Applied Energy, Elsevier, vol. 263(C).
    3. Sun, Guoxin & Yu, Yongheng & Yu, Qihui & Tan, Xin & Wu, Linfeng & Wang, Yahui, 2024. "Enhancing control and performance evaluation of composite heating systems through modal analysis and model predictive control: Design and comprehensive analysis," Applied Energy, Elsevier, vol. 357(C).
    4. Leprince, Julien & Madsen, Henrik & Møller, Jan Kloppenborg & Zeiler, Wim, 2023. "Hierarchical learning, forecasting coherent spatio-temporal individual and aggregated building loads," Applied Energy, Elsevier, vol. 348(C).
    5. Ramos, Paulo Vitor B. & Villela, Saulo Moraes & Silva, Walquiria N. & Dias, Bruno H., 2023. "Residential energy consumption forecasting using deep learning models," Applied Energy, Elsevier, vol. 350(C).
    6. Yupeng Wang & Hiroatsu Fukuda, 2019. "The Influence of Insulation Styles on the Building Energy Consumption and Indoor Thermal Comfort of Multi-Family Residences," Sustainability, MDPI, vol. 11(1), pages 1-14, January.
    7. 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.
    8. Hee, W.J. & Alghoul, M.A. & Bakhtyar, B. & Elayeb, OmKalthum & Shameri, M.A. & Alrubaih, M.S. & Sopian, K., 2015. "The role of window glazing on daylighting and energy saving in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 323-343.
    9. Cai, Mengmeng & Pipattanasomporn, Manisa & Rahman, Saifur, 2019. "Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques," Applied Energy, Elsevier, vol. 236(C), pages 1078-1088.
    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. Zhang, Xu & Sun, Yongjun & Gao, Dian-ce & Zou, Wenke & Fu, Jianping & Ma, Xiaowen, 2022. "Similarity-based grouping method for evaluation and optimization of dataset structure in machine-learning based short-term building cooling load prediction without measurable occupancy information," Applied Energy, Elsevier, vol. 327(C).
    2. Wei, Ziqing & Zhang, Tingwei & Yue, Bao & Ding, Yunxiao & Xiao, Ran & Wang, Ruzhu & Zhai, Xiaoqiang, 2021. "Prediction of residential district heating load based on machine learning: A case study," Energy, Elsevier, vol. 231(C).
    3. Dalia Mohammed Talat Ebrahim Ali & Violeta Motuzienė & Rasa Džiugaitė-Tumėnienė, 2024. "AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings," Energies, MDPI, vol. 17(17), pages 1-35, August.
    4. Li, Sihui & Peng, Jinqing & Zou, Bin & Li, Bojia & Lu, Chujie & Cao, Jingyu & Luo, Yimo & Ma, Tao, 2021. "Zero energy potential of photovoltaic direct-driven air conditioners with considering the load flexibility of air conditioners," Applied Energy, Elsevier, vol. 304(C).
    5. Ng, Rong Wang & Begam, Kasim Mumtaj & Rajkumar, Rajprasad Kumar & Wong, Yee Wan & Chong, Lee Wai, 2021. "An improved self-organizing incremental neural network model for short-term time-series load prediction," Applied Energy, Elsevier, vol. 292(C).
    6. Jason Runge & Radu Zmeureanu, 2021. "A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings," Energies, MDPI, vol. 14(3), pages 1-26, January.
    7. 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.
    8. Zhang, Chaobo & Li, Junyang & Zhao, Yang & Li, Tingting & Chen, Qi & Zhang, Xuejun & Qiu, Weikang, 2021. "Problem of data imbalance in building energy load prediction: Concept, influence, and solution," Applied Energy, Elsevier, vol. 297(C).
    9. Liu, Jiangyan & Zhang, Qing & Dong, Zhenxiang & Li, Xin & Li, Guannan & Xie, Yi & Li, Kuining, 2021. "Quantitative evaluation of the building energy performance based on short-term energy predictions," Energy, Elsevier, vol. 223(C).
    10. Yuwen You & Zhonghua Wang & Zhihao Liu & Chunmei Guo & Bin Yang, 2024. "Load Prediction of Regional Heat Exchange Station Based on Fuzzy Clustering Based on Fourier Distance and Convolutional Neural Network–Bidirectional Long Short-Term Memory Network," Energies, MDPI, vol. 17(16), pages 1-19, August.
    11. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    12. Alexandra L’Heureux & Katarina Grolinger & Miriam A. M. Capretz, 2022. "Transformer-Based Model for Electrical Load Forecasting," Energies, MDPI, vol. 15(14), pages 1-23, July.
    13. Nikos Sakkas & Sofia Yfanti & Pooja Shah & Nikitas Sakkas & Christina Chaniotakis & Costas Daskalakis & Eduard Barbu & Marharyta Domnich, 2023. "Explainable Approaches for Forecasting Building Electricity Consumption," Energies, MDPI, vol. 16(20), pages 1-20, October.
    14. Michaux, Ghislain & Greffet, Rémy & Salagnac, Patrick & Ridoret, Jean-Baptiste, 2019. "Modelling of an airflow window and numerical investigation of its thermal performances by comparison to conventional double and triple-glazed windows," Applied Energy, Elsevier, vol. 242(C), pages 27-45.
    15. Karolis Banionis & Jurga Kumžienė & Arūnas Burlingis & Juozas Ramanauskas & Valdas Paukštys, 2021. "The Changes in Thermal Transmittance of Window Insulating Glass Units Depending on Outdoor Temperatures in Cold Climate Countries," Energies, MDPI, vol. 14(6), pages 1-22, March.
    16. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
    17. Seok-Hyun Kim & Hakgeun Jeong & Soo Cho, 2019. "A Study on Changes of Window Thermal Performance by Analysis of Physical Test Results in Korea," Energies, MDPI, vol. 12(20), pages 1-17, October.
    18. Jonathan Berrisch & Micha{l} Narajewski & Florian Ziel, 2022. "High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks," Papers 2203.03342, arXiv.org, revised Nov 2022.
    19. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
    20. Rafael Herrera-Limones & Ángel Luis León-Rodríguez & Álvaro López-Escamilla, 2019. "Solar Decathlon Latin America and Caribbean: Comfort and the Balance between Passive and Active Design," Sustainability, MDPI, vol. 11(13), pages 1-17, June.

    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:eee:appene:v:377:y:2025:i:pc:s0306261924020038. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.