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Performance Evaluation Method of Day-Ahead Load Prediction Models in a District Heating and Cooling System: A Case Study

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  • Haiyan Meng

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
    Cecep Wind Power Co., Ltd., Beijing 100082, China)

  • Yakai Lu

    (School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China)

  • Zhe Tian

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
    Tianjin Key Laboratory of Building Environment and Energy, Tianjin 300072, China)

  • Xiangbei Jiang

    (Cecep Wind Power Co., Ltd., Beijing 100082, China)

  • Zhongqing Han

    (State Grid Jibei Power Co., Ltd., Beijing 100054, China)

  • Jide Niu

    (School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
    Tianjin Key Laboratory of Building Environment and Energy, Tianjin 300072, China)

Abstract

Many researchers are devoted to improving the prediction accuracy of daily load profiles, so as to optimize day-ahead operation strategies to achieve the most efficient operation of district heating and cooling (DHC) systems; however, studies on load prediction and operation strategy optimization are generally isolated, which leaves the following question: what day-head load prediction performance should be paid attention to in the operation optimization of DHC systems? In order to explain this issue, and taking an actual DHC system as a case study, this paper proposes an evaluation method for the prediction of daily cooling load profiles by considering the impact of inaccurate prediction on the operation of a DHC system. The evaluation results show the following: (1) When prediction models for daily load profiles are developed, the prediction accuracy of the daily mean load should be emphasized, and there is no need to painstakingly increase the accuracy of load profile shapes. (2) CV and RMSE are the most suitable deviation measures (compared to others, e.g., MAPE, MAE, etc.) for the evaluation of load prediction models. A prediction model with 27.8% deviation ( CV ) only causes a 3.74% deviation in operation costs; thus, the prediction performance is enough to meet the engineering requirements for the DHC system in this paper.

Suggested Citation

  • Haiyan Meng & Yakai Lu & Zhe Tian & Xiangbei Jiang & Zhongqing Han & Jide Niu, 2023. "Performance Evaluation Method of Day-Ahead Load Prediction Models in a District Heating and Cooling System: A Case Study," Energies, MDPI, vol. 16(14), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:14:p:5402-:d:1194954
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    References listed on IDEAS

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