IDEAS home Printed from https://ideas.repec.org/a/sae/engenv/v37y2026i1p282-310.html

Forecasting the total building energy based on its architectural features using a combination of CatBoost and meta-heuristic algorithms

Author

Listed:
  • Xiaoyu Qu
  • Ziheng Liu

Abstract

This research examines the overall energy usage in residential buildings, focusing on architectural characteristics. The study utilizes a combination of the CatBoost method and meta-heuristic algorithms for analysis. The main approach of this research is based on the accuracy defects of individual models, which leads to the employment of CatBoost as a group model. Due to the lack of enough examinations while utilizing CatBoost method, this model and its hyperparameters are optimized using various meta-heuristic methods, including Phasor Particle Swarm Optimization (PPSO), Slime Mould Algorithm (SMA), Sparrow Search Algorithm (SSA), Ant Lion Optimizer (ALO), Artificial Bee Colony (ABC), and Grey Wolf Optimizer (GWO). Eventually, the performance of all models is compared by conduction of a case study, using diverse statistical examination indexes divided by the dwelling types i.e., (1) Standard efficiency upgraded dwellings (D1), (2) High efficiency upgraded dwellings (D2), and (3) Ultra high efficiency upgraded dwellings (D3). The results show that the hybrid proposed method has a proper ability to investigate the total site energy. The results show that for the D1 dwelling and according to the test dataset, the integrated CatBoost-SMA model indicates the most desired performance in predicting the total site energy. But for D2 and D3 dwellings and referring to the test dataset, the statistical evaluation indexes emphasize that the integrated CatBoost-PPSO method shows the most reliable performance.

Suggested Citation

  • Xiaoyu Qu & Ziheng Liu, 2026. "Forecasting the total building energy based on its architectural features using a combination of CatBoost and meta-heuristic algorithms," Energy & Environment, , vol. 37(1), pages 282-310, February.
    • Handle: RePEc:sae:engenv:v:37:y:2026:i:1:p:282-310
    DOI: 10.1177/0958305X241241029
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0958305X241241029
    Download Restriction: no
    File URL: https://libkey.io/10.1177/0958305X241241029?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
    ---><---

    References listed on IDEAS

    as
    1. Bui, Dac-Khuong & Nguyen, Tuan Ngoc & Ngo, Tuan Duc & Nguyen-Xuan, H., 2020. "An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings," Energy, Elsevier, vol. 190(C).
    2. Amber, K.P. & Ahmad, R. & Aslam, M.W. & Kousar, A. & Usman, M. & Khan, M.S., 2018. "Intelligent techniques for forecasting electricity consumption of buildings," Energy, Elsevier, vol. 157(C), pages 886-893.
    3. Cinzia Buratti & Elisa Lascaro & Domenico Palladino & Marco Vergoni, 2014. "Building Behavior Simulation by Means of Artificial Neural Network in Summer Conditions," Sustainability, MDPI, vol. 6(8), pages 1-15, August.
    4. Ghadiri, Mehdi & Rassafi, Amir Abbas & Mirbaha, Babak, 2019. "The effects of traffic zoning with regular geometric shapes on the precision of trip production models," Journal of Transport Geography, Elsevier, vol. 78(C), pages 150-159.
    5. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
    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. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    2. Linlin Zhao & Zhansheng Liu & Jasper Mbachu, 2019. "Energy Management through Cost Forecasting for Residential Buildings in New Zealand," Energies, MDPI, vol. 12(15), pages 1-24, July.
    3. 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).
    4. Melillo, Andreas & Durrer, Roman & Worlitschek, Jörg & Schütz, Philipp, 2020. "First results of remote building characterisation based on smart meter measurement data," Energy, Elsevier, vol. 200(C).
    5. Cai, Wei & Wen, Xiaodong & Li, Chaoen & Shao, Jingjing & Xu, Jianguo, 2023. "Predicting the energy consumption in buildings using the optimized support vector regression model," Energy, Elsevier, vol. 273(C).
    6. Cao, Wenqiang & Yu, Junqi & Chao, Mengyao & Wang, Jingqi & Yang, Siyuan & Zhou, Meng & Wang, Meng, 2023. "Short-term energy consumption prediction method for educational buildings based on model integration," Energy, Elsevier, vol. 283(C).
    7. Łukasz Guz & Dariusz Gaweł & Tomasz Cholewa & Alicja Siuta-Olcha & Martyna Bocian & Mariia Liubarska, 2025. "Forecasting Heat Power Demand in Retrofitted Residential Buildings," Energies, MDPI, vol. 18(3), pages 1-26, February.
    8. Chen, Yibo & Zhang, Fengyi & Berardi, Umberto, 2020. "Day-ahead prediction of hourly subentry energy consumption in the building sector using pattern recognition algorithms," Energy, Elsevier, vol. 211(C).
    9. Adinkrah, Julius & Kemausuor, Francis & Tutu Tchao, Eric & Nunoo-Mensah, Henry & Agbemenu, Andrew Selasi & Adu-Poku, Akwasi & Kponyo, Jerry John, 2025. "Artificial intelligence-based strategies for sustainable energy planning and electricity demand estimation: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).
    10. Zheng, Li & Abbasi, Kashif Raza & Salem, Sultan & Irfan, Muhammad & Alvarado, Rafael & Lv, Kangjuan, 2022. "How technological innovation and institutional quality affect sectoral energy consumption in Pakistan? Fresh policy insights from novel econometric approach," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    11. Yuo-Hsien Shiau & Su-Fen Yang & Rishan Adha & Syamsiyatul Muzayyanah, 2022. "Modeling Industrial Energy Demand in Relation to Subsector Manufacturing Output and Climate Change: Artificial Neural Network Insights," Sustainability, MDPI, vol. 14(5), pages 1-18, March.
    12. Ting Jin & Rui Xu & Kunqi Su & Jinrui Gao, 2025. "A Dendritic Neural Network-Based Model for Residential Electricity Consumption Prediction," Mathematics, MDPI, vol. 13(4), pages 1-23, February.
    13. Khalid Almutairi & Salem Algarni & Talal Alqahtani & Hossein Moayedi & Amir Mosavi, 2022. "A TLBO-Tuned Neural Processor for Predicting Heating Load in Residential Buildings," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    14. Tian, Shen & Shao, Shuangquan & Liu, Bin, 2019. "Investigation on transient energy consumption of cold storages: Modeling and a case study," Energy, Elsevier, vol. 180(C), pages 1-9.
    15. Rongjiang Ma & Shen Yang & Xianlin Wang & Xi-Cheng Wang & Ming Shan & Nanyang Yu & Xudong Yang, 2020. "Systematic Method for the Energy-Saving Potential Calculation of Air-Conditioning Systems via Data Mining. Part I: Methodology," Energies, MDPI, vol. 14(1), pages 1-15, December.
    16. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    17. Luo, X.J. & Oyedele, Lukumon O. & Ajayi, Anuoluwapo O. & Akinade, Olugbenga O. & Owolabi, Hakeem A. & Ahmed, Ashraf, 2020. "Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    18. Meng Wang & Junqi Yu & Meng Zhou & Wei Quan & Renyin Cheng, 2023. "Joint Forecasting Model for the Hourly Cooling Load and Fluctuation Range of a Large Public Building Based on GA-SVM and IG-SVM," Sustainability, MDPI, vol. 15(24), pages 1-23, December.
    19. Fang, Lei & He, Bin, 2023. "A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting," Applied Energy, Elsevier, vol. 348(C).
    20. Buratti, C. & Palladino, D. & Ricciardi, P., 2016. "Application of a new 13-value thermal comfort scale to moderate environments," Applied Energy, Elsevier, vol. 180(C), pages 859-866.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:sae:engenv:v:37:y:2026:i:1:p:282-310. 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: SAGE Publications (email available below). General contact details of provider: .

    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.