Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Short-Term Wind Speed Forecast Based on Least Squares Support Vector Machine

  • Wang, Yanling (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Zhou, Xing (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Liang, Likai (School of Mechanical, Electrical and Information Engineering, Shandong University) ;
  • Zhang, Mingjun (State Grid Jiamusi Power Supply Co. Ltd.) ;
  • Zhang, Qiang (Shandong Electric Power Dispatching Control Center) ;
  • Niu, Zhiqiang (State Grid Weihai Power Supply Company)
  • Received : 2017.04.25
  • Accepted : 2017.05.18
  • Published : 2018.12.31
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Abstract

There are many factors that affect the wind speed. In addition, the randomness of wind speed also leads to low prediction accuracy for wind speed. According to this situation, this paper constructs the short-time forecasting model based on the least squares support vector machines (LSSVM) to forecast the wind speed. The basis of the model used in this paper is support vector regression (SVR), which is used to calculate the regression relationships between the historical data and forecasting data of wind speed. In order to improve the forecast precision, historical data is clustered by cluster analysis so that the historical data whose changing trend is similar with the forecasting data can be filtered out. The filtered historical data is used as the training samples for SVR and the parameters would be optimized by particle swarm optimization (PSO). The forecasting model is tested by actual data and the forecast precision is more accurate than the industry standards. The results prove the feasibility and reliability of the model.

Keywords

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Fig. 1. Short-term wind speed forecasting model.

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Fig. 2. Data of the 10-day historical wind speed.

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Fig. 3. Daily variation of wind speed curve of category I.

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Fig. 4. Daily variation of wind speed curve of category II.

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Fig. 5. Daily variation of wind speed curve of category III.

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Fig. 6. Predicted value of wind speed and actual value of wind speed.

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Fig. 7. Relative error of prediction point.

Table 1. History data classification results

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Table 2. The comparison of input variables

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Table 3. Parameters of particle swarm optimization algorithm

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