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A hybrid deep learning model based on Rime optimization and multi-head attention for cooling load prediction in public buildings

Author

Listed:
  • Feng, Zengxi
  • Zhang, Xian
  • Quan, Wei
  • Liu, Xuefeng
  • An, Jianhu
  • Wang, Chang
  • Ji, Xiuming
  • Kang, Limin

Abstract

Accurate prediction of cooling load is a prerequisite for the control of air conditioning systems and is of great significance for energy saving in buildings. This study proposes a hybrid deep learning model (AHRIME-CNN-BiLSTM-MHSA) based on Rime optimization and Multi-Head Attention for cooling load prediction in large public buildings. Firstly, an importance analysis of input features is conducted using random forests, and key features are selected to reduce the dimensionality of input feature parameters. Secondly, a hybrid deep learning model is established by integrating data extraction with Convolutional Neural Network (CNN), bidirectional learning of temporal features with Bidirectional Long Short-Term Memory (BiLSTM), and the mining of key information with the Multi-Head Self-Attention mechanism (MHSA). Based on this, the improved Adaptive Hybrid Rime Optimization algorithm (AHRIME) is employed to optimize the hybrid model, determining the AHRIME-CNN-BiLSTM-MHSA model with the optimal combination of parameters. Finally, the performance of the proposed cooling load prediction model is validated using actual data by establishing control groups. The results indicate that CNN and MHSA can enhance the effectiveness of BiLSTM in processing information and improve prediction accuracy. AHRIME can obtain the optimal parameters of the model, thereby enhancing predictive performance.

Suggested Citation

  • Feng, Zengxi & Zhang, Xian & Quan, Wei & Liu, Xuefeng & An, Jianhu & Wang, Chang & Ji, Xiuming & Kang, Limin, 2025. "A hybrid deep learning model based on Rime optimization and multi-head attention for cooling load prediction in public buildings," Energy, Elsevier, vol. 339(C).
    • Handle: RePEc:eee:energy:v:339:y:2025:i:c:s0360544225047425
    DOI: 10.1016/j.energy.2025.139100
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    References listed on IDEAS

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