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Space cooling energy usage prediction based on utility data for residential buildings using machine learning methods

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  • Feng, Yanxiao
  • Duan, Qiuhua
  • Chen, Xi
  • Yakkali, Sai Santosh
  • Wang, Julian

Abstract

The energy used for space cooling in residential buildings has a significant influence on household energy performance. This study aims to develop a user-friendly, infrastructure-free, and accurate prediction model based on large-scale utility datasets from anonymized volunteer homes located in three different climate zones in the US, along with the corresponding weather data and building information. Notably, several new weather- and building characteristics-related parameters were designed in the modeling procedure and tested to be useful for enhancing the model’s prediction performance. A few regression techniques were examined and compared through hyperparameter optimization and k-fold cross-validation. Subsequently, a workflow was also described for how to implement the developed model. The research results showed that the eXtreme Gradient Boosting (XGBoost) model offered optimal performance, and the feature importance analysis also identified as well as ranked the key predictors to enhance the interpretability of this model. An R2 value of around 97% was obtained with that model on the whole dataset, while an R2 value of 92% was achieved with various subsets of the dataset through the cross-validation approach. The RMSE and RAE for this model were 0.294 and 0.153, respectively. The resultant model for predicting cooling energy consumption will facilitate homeowners better understanding their buildings’ performance levels with minimum input information and without additional hardware installations, ultimately aiding their decision making related to energy-saving strategies.

Suggested Citation

  • Feng, Yanxiao & Duan, Qiuhua & Chen, Xi & Yakkali, Sai Santosh & Wang, Julian, 2021. "Space cooling energy usage prediction based on utility data for residential buildings using machine learning methods," Applied Energy, Elsevier, vol. 291(C).
    • Handle: RePEc:eee:appene:v:291:y:2021:i:c:s0306261921003159
    DOI: 10.1016/j.apenergy.2021.116814
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    References listed on IDEAS

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    Cited by:

    1. Tang, Lingfeng & Xie, Haipeng & Wang, Xiaoyang & Bie, Zhaohong, 2023. "Privacy-preserving knowledge sharing for few-shot building energy prediction: A federated learning approach," Applied Energy, Elsevier, vol. 337(C).
    2. Massimiliano Manfren & Karla M. Gonzalez-Carreon & Patrick A. B. James, 2024. "Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps," Energies, MDPI, vol. 17(4), pages 1-22, February.
    3. Kesriklioğlu, Esma & Oktay, Erkan & Karaaslan, Abdulkerim, 2023. "Predicting total household energy expenditures using ensemble learning methods," Energy, Elsevier, vol. 276(C).
    4. Qin, Haosen & Yu, Zhen & Li, Tailu & Liu, Xueliang & Li, Li, 2023. "Energy-efficient heating control for nearly zero energy residential buildings with deep reinforcement learning," Energy, Elsevier, vol. 264(C).
    5. Sun, Jian & Liu, Gang & Sun, Boyang & Xiao, Gang, 2021. "Light-stacking strengthened fusion based building energy consumption prediction framework via variable weight feature selection," Applied Energy, Elsevier, vol. 303(C).
    6. Sui, Zengguang & Sui, Yunren & Wu, Wei, 2022. "Multi-objective optimization of a microchannel membrane-based absorber with inclined grooves based on CFD and machine learning," Energy, Elsevier, vol. 240(C).
    7. Razak Olu-Ajayi & Hafiz Alaka & Hakeem Owolabi & Lukman Akanbi & Sikiru Ganiyu, 2023. "Data-Driven Tools for Building Energy Consumption Prediction: A Review," Energies, MDPI, vol. 16(6), pages 1-20, March.
    8. Feng, Yanxiao & Liu, Shichao & Wang, Julian & Yang, Jing & Jao, Ying-Ling & Wang, Nan, 2022. "Data-driven personal thermal comfort prediction: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).

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