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HVAC Load Forecasting Based on the CEEMDAN-Conv1D-BiLSTM-AM Model

Author

Listed:
  • Zhicheng Xiao

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Lijuan Yu

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Huajun Zhang

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Xuetao Zhang

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

  • Yixin Su

    (School of Automation, Wuhan University of Technology, Wuhan 430070, China)

Abstract

Heating, ventilation, and air-conditioning (HVAC) systems consume approximately 60% of the total energy consumption in public buildings, and an effective way to reduce HVAC energy consumption is to provide accurate load forecasting. This paper proposes a load forecasting model CEEMDAN-Conv1D-BiLSTM-AM which combines empirical mode decomposition and neural networks. The load data are decomposed into fifteen sub-sequences using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The neural network inputs consist of the decomposition results and five exogenous variables. The neural networks contain a one-dimensional convolutional layer, a BiLSTM layer, and an attention mechanism layer. The Conv1D is employed to extract deep features from each input variable, while BiLSTM and the attention mechanism layer are used to learn the characteristics of the load time series. The five exogenous variables are selected based on the correlation analysis between external factors and load series, and the number of input steps for the model is determined through autocorrelation analysis of the load series. The performance of CEEMDAN-Conv1D-BiLSTM-AM is compared with that of five other models and the results show that the proposed model has a higher prediction accuracy than other models.

Suggested Citation

  • Zhicheng Xiao & Lijuan Yu & Huajun Zhang & Xuetao Zhang & Yixin Su, 2023. "HVAC Load Forecasting Based on the CEEMDAN-Conv1D-BiLSTM-AM Model," Mathematics, MDPI, vol. 11(22), pages 1-24, November.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:22:p:4630-:d:1279070
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    References listed on IDEAS

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    Cited by:

    1. Xinfu Liu & Wei Liu & Wei Zhou & Yanfeng Cao & Mengxiao Wang & Wenhao Hu & Chunhua Liu & Peng Liu & Guoliang Liu, 2024. "Multi-Energy Coupling Load Forecasting in Integrated Energy System with Improved Variational Mode Decomposition-Temporal Convolutional Network-Bidirectional Long Short-Term Memory Model," Sustainability, MDPI, vol. 16(22), pages 1-18, November.

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