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A Weekend Load Forecasting Model Based on Semi-Parametric Regression Analysis Considering Weather and Load Interaction

Author

Listed:
  • Bin Li

    (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China)

  • Mingzhen Lu

    (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China)

  • Yiyi Zhang

    (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China)

  • Jia Huang

    (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China)

Abstract

Compared to the load characteristics of normal working days, weekend load characteristics have a low level of load and are sensitive to meteorological conditions, which influences the accuracy of short-term weekend-load forecasting. To solve this problem and to improve the accuracy of short-term weekend-load forecasting, a Semi-parametric weekend-load forecasting method based on the interaction between meteorological and load is proposed in this paper. The main work is shown as follows: (1) through separating weekend-load from normal-load and analyzing the correlation between meteorological factors and daily maximum load, the meteorological factors with parameter characteristics and non-parameter characteristics can be screened out; (2) a short-term weekend-load forecasting model is built according to Semi-parametric regression theory which can express the coupling relation between meteorology and load more realistically; (3) the effect of temperature accumulation is also considered to correct the forecasting model. The proposed method is proved by implementing short-term weekend-load forecasting on the real historical data of the Southern Power Grid in China. The result shows that the 96-point mean load forecasting accuracy obtained by this model can meet the requirement of power network operation.

Suggested Citation

  • Bin Li & Mingzhen Lu & Yiyi Zhang & Jia Huang, 2019. "A Weekend Load Forecasting Model Based on Semi-Parametric Regression Analysis Considering Weather and Load Interaction," Energies, MDPI, vol. 12(20), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:3820-:d:274717
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    References listed on IDEAS

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    Cited by:

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    2. Kamal Chapagain & Somsak Kittipiyakul & Pisut Kulthanavit, 2020. "Short-Term Electricity Demand Forecasting: Impact Analysis of Temperature for Thailand," Energies, MDPI, vol. 13(10), pages 1-29, May.
    3. Dengyong Zhang & Haixin Tong & Feng Li & Lingyun Xiang & Xiangling Ding, 2020. "An Ultra-Short-Term Electrical Load Forecasting Method Based on Temperature-Factor-Weight and LSTM Model," Energies, MDPI, vol. 13(18), pages 1-14, September.

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