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Research on Central Air Conditioning Systems and an Intelligent Prediction Model of Building Energy Load

Author

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  • Lin Pan

    (School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
    GREE, State Key Laboratory of Air-Conditioning Equipment and System Energy Conservation, GREE Electric Appliances Inc. of Zhuhai, Zhuhai 519070, China
    Hainan Institute, Wuhan University of Technology, Sanya 572025, China
    Hebei Huifeng Network Technology Development Co., Ltd., Shijiazhuang 050092, China)

  • Sheng Wang

    (GREE, State Key Laboratory of Air-Conditioning Equipment and System Energy Conservation, GREE Electric Appliances Inc. of Zhuhai, Zhuhai 519070, China
    These authors contributed equally to this work.)

  • Jiying Wang

    (School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
    These authors contributed equally to this work.)

  • Min Xiao

    (School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430063, China
    These authors contributed equally to this work.)

  • Zhirong Tan

    (School of Navigation, Wuhan University of Technology, Wuhan 430063, China
    Hubei Key Laboratory of Inland Shipping Technology, Wuhan 430063, China
    These authors contributed equally to this work.)

Abstract

The central air conditioning system provides city dwellers with an efficient and comfortable environment. Meanwhile, coinciding with their use, the building electricity load is increased, as central air conditioners consume a lot of electricity. It has become necessary to control central air conditioners for storage and to analyze the energy saving optimization of central air conditioner operation. This study investigates the energy consumption background of central air conditioning systems, and proposes an intelligent load prediction method. With a back propagation (BP) neural network, we use the data collected in the actual project to build the cooling load prediction model for central air conditioning. The network model is also trained using the Levenberg–Marquardt (LM) algorithm, and the established model is trained, tested, and predicted by importing a portion of the sample data, which is filtered by preprocessing. The experimental results show that most of the data errors for training, testing, and prediction are within 10%, indicating that the accuracy achievable by the model can meet the practical requirements, and can be used in real engineering projects.

Suggested Citation

  • Lin Pan & Sheng Wang & Jiying Wang & Min Xiao & Zhirong Tan, 2022. "Research on Central Air Conditioning Systems and an Intelligent Prediction Model of Building Energy Load," Energies, MDPI, vol. 15(24), pages 1-31, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9295-:d:996781
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    References listed on IDEAS

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    1. Siyue Lu & Baoqun Zhang & Longfei Ma & Hui Xu & Yuantong Li & Shaobing Yang, 2023. "Economic Load-Reduction Strategy of Central Air Conditioning Based on Convolutional Neural Network and Pre-Cooling," Energies, MDPI, vol. 16(13), pages 1-22, June.

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