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Air Conditioning Load Forecasting and Optimal Operation of Water Systems

Author

Listed:
  • Zhijia Huang

    (School of Civil Engineering and Architecture, Anhui University of Technology, Ma’anshan 243032, China)

  • Xiaofeng Chen

    (School of Civil Engineering and Architecture, Anhui University of Technology, Ma’anshan 243032, China)

  • Kaiwen Wang

    (Hangzhou RUNPAQ Environment & Engineering Co., Ltd., Hangzhou 310051, China)

  • Binbin Zhou

    (School of Civil Engineering and Architecture, Anhui University of Technology, Ma’anshan 243032, China)

Abstract

In order to conduct a data-driven load forecasting modeling and its application in optimal control of air-conditioning system, this study used a hotel’s central air conditioning system as the research object. Based on the data of the hotel energy management system, the load-forecasting model of the central air conditioning system based on support vector regression (SVR) was established by MATLAB. Based on the working principle of a chiller, chilled water pump, cooling water pump, and cooling tower, the energy consumption models were established, respectively. Finally, based on the load-forecasting results and the equipment energy consumption model, the energy consumption optimization objective function of the hotel water system was established, the objective function was solved to optimize the operating parameters of the water system at different load rates, the operation control strategy for each piece of equipment was obtained, and the energy-saving analysis was carried out. The results show that in the range of a load rate of 25~90%, the optimization strategy has an energy-saving effect, and the system’s energy-saving rate is the highest when the load rate is 25.4%. The average energy-saving rate of the system is 12.4%.

Suggested Citation

  • Zhijia Huang & Xiaofeng Chen & Kaiwen Wang & Binbin Zhou, 2022. "Air Conditioning Load Forecasting and Optimal Operation of Water Systems," Sustainability, MDPI, vol. 14(9), pages 1-12, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:4867-:d:796665
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    References listed on IDEAS

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    1. Deb, C. & Schlueter, A., 2021. "Review of data-driven energy modelling techniques for building retrofit," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    2. Yan, Chengchu & Wang, Shengwei & Xiao, Fu & Gao, Dian-ce, 2015. "A multi-level energy performance diagnosis method for energy information poor buildings," Energy, Elsevier, vol. 83(C), pages 189-203.
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    Cited by:

    1. Fu-Wing Yu & Wai-Tung Ho, 2023. "Time Series Forecast of Cooling Demand for Sustainable Chiller System in an Office Building in a Subtropical Climate," Sustainability, MDPI, vol. 15(8), pages 1-18, April.
    2. Rodrigo Schons Arenhart & Adriano Mendonça Souza & Roselaine Ruviaro Zanini, 2022. "Energy Use and Its Key Factors in Hotel Chains," Sustainability, MDPI, vol. 14(14), pages 1-14, July.

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