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ZTE Communications ›› 2015, Vol. 13 ›› Issue (3): 2-5.DOI: 10.3969/j.issn.1673-5188.2015.03.001

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Theory Study and Application of the BP-ANN Method for Power Grid Short-Term Load Forecasting

Xia Hua1, Gang Zhang2, Jiawei Yang3, Zhengyuan Li1   

  1. 1. Gansu Electric Power Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730050, China;
    2. Institute of Water Resources and Hydro-Electric Engineering, Xi ’an University of Technology Xi ’an 710048, China;
    3. College of International Communications, China Three Gorges University, Yichang 443000, China
  • 收稿日期:2015-04-13 出版日期:2015-09-25 发布日期:2015-09-25
  • 作者简介:Xia Hua (kevinxhua@163.com) received the bachelor degree in physics from Shanghai Jiao Tong University in 2009. He received his PhD degree in semiconductor physics from the Department of Physics and Astronomy, Shanghai Jiao Tong University from the Department of Physics and Astronomy, Shanghai Jiao Tong University in 2014. He is currently working in the Gansu Electric Power Research Institute, Lanzhou, China. His research interest focuses on new energy and photovoltaic systems.
    Gang Zhang (zhanggang3463003@xaut.edu.cn) received his PhD degree in water resources and hydrology from Xi′an University of Technology in 2013. He is currently working in the Institute of Water Resources and Hydro-electric Engineering, Xi′ an University of Technology. His research interest focuses on new energy and power saving.
    Jiawei Yang is currently pursuing the bachelor’s degree in Electrical Engineering and automation. His current interests include smart grid systems.
    Zhengyuan Li is currently working in the Gansu Electric Power Research Institute, Lanzhou, China. His research interest focuses on power system protection.

Theory Study and Application of the BP-ANN Method for Power Grid Short-Term Load Forecasting

Xia Hua1, Gang Zhang2, Jiawei Yang3, Zhengyuan Li1   

  1. 1. Gansu Electric Power Research Institute, State Grid Gansu Electric Power Company, Lanzhou 730050, China;
    2. Institute of Water Resources and Hydro-Electric Engineering, Xi ’an University of Technology Xi ’an 710048, China;
    3. College of International Communications, China Three Gorges University, Yichang 443000, China
  • Received:2015-04-13 Online:2015-09-25 Published:2015-09-25
  • About author:Xia Hua (kevinxhua@163.com) received the bachelor degree in physics from Shanghai Jiao Tong University in 2009. He received his PhD degree in semiconductor physics from the Department of Physics and Astronomy, Shanghai Jiao Tong University from the Department of Physics and Astronomy, Shanghai Jiao Tong University in 2014. He is currently working in the Gansu Electric Power Research Institute, Lanzhou, China. His research interest focuses on new energy and photovoltaic systems.
    Gang Zhang (zhanggang3463003@xaut.edu.cn) received his PhD degree in water resources and hydrology from Xi′an University of Technology in 2013. He is currently working in the Institute of Water Resources and Hydro-electric Engineering, Xi′ an University of Technology. His research interest focuses on new energy and power saving.
    Jiawei Yang is currently pursuing the bachelor’s degree in Electrical Engineering and automation. His current interests include smart grid systems.
    Zhengyuan Li is currently working in the Gansu Electric Power Research Institute, Lanzhou, China. His research interest focuses on power system protection.

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摘要： Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network (BP-ANN) based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid (China) based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.

关键词: BP-ANN, short-term load forecasting of power grid, multiscale entropy, correlation analysis

Abstract: Aiming at the low accuracy problem of power system short-term load forecasting by traditional methods, a back-propagation artificial neural network (BP-ANN) based method for short-term load forecasting is presented in this paper. The forecast points are related to prophase adjacent data as well as the periodical long-term historical load data. Then the short-term load forecasting model of Shanxi Power Grid (China) based on BP-ANN method and correlation analysis is established. The simulation model matches well with practical power system load, indicating the BP-ANN method is simple and with higher precision and practicality.

Key words: BP-ANN, short-term load forecasting of power grid, multiscale entropy, correlation analysis