Journal of Electrical Engineering and Technology

The Korean Institute of Electrical Engineers (대한전기학회)
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Short-Term Photovoltaic Power Generation Forecasting Based on Environmental Factors and GA-SVM

  • Wang, Jidong (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University) ;
  • Ran, Ran (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University) ;
  • Song, Zhilin (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University) ;
  • Sun, Jiawen (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University)
  • Received : 2016.04.26
  • Accepted : 2016.09.16
  • Published : 2017.01.02
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Abstract

Considering the volatility, intermittent and random of photovoltaic (PV) generation systems, accurate forecasting of PV power output is important for the grid scheduling and energy management. In order to improve the accuracy of short-term power forecasting of PV systems, this paper proposes a prediction model based on environmental factors and support vector machine optimized by genetic algorithm (GA-SVM). In order to improve the prediction accuracy of this model, weather conditions are divided into three types, and the gray correlation coefficient algorithm is used to find out a similar day of the predicted day. To avoid parameters optimization into local optima, this paper uses genetic algorithm to optimize SVM parameters. Example verification shows that the prediction accuracy in three types of weather will remain at between 10% -15% and the short-term PV power forecasting model proposed is effective and promising.

Keywords

Acknowledgement

Supported by : National Natural Science Foundation of China (NSFC)

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