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Short-term energy consumption prediction method for educational buildings based on model integration

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Listed:
  • Cao, Wenqiang
  • Yu, Junqi
  • Chao, Mengyao
  • Wang, Jingqi
  • Yang, Siyuan
  • Zhou, Meng
  • Wang, Meng

Abstract

Paying attention to the feature engineering problems is the basis for constructing a more accurate building energy consumption prediction model, which helps debug, control, and operate building energy management systems. Therefore, in this paper, an integrated energy consumption prediction model considering spatial characteristics in time series data is proposed to predict the short-term energy consumption of educational buildings, and the influence of features on the model is analyzed using the cooperative game theory SHAP method, and the optimal number of features is determined by ablation analysis. The proposed model is validated by an educational building in Xi'an, Shaanxi Province. The results show that compared with other energy consumption prediction models, the RMSE value of the integrated energy consumption prediction model is reduced by 13.64%–34.55%, and the MAE value is reduced by 10.25%–30.54%, which has higher prediction accuracy. In addition, this paper also investigates the minimum amount of data and the number of features required for the training of the building energy prediction model, and the integrated energy prediction model can still effectively predict building energy consumption when the training samples are minimal and the number of features is appropriate.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223019746
    DOI: 10.1016/j.energy.2023.128580
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    2. Qingwen, Wang & XiaoHui, Chu & Chao, Yu, 2024. "Modeling of heat gain through green roofs utilizing artificial intelligence techniques," Energy, Elsevier, vol. 303(C).

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