Short-Term Wind Speed Forecast Based on Least Squares Support Vector Machine
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초록
There are many factors that affect the wind speed. In addition, the randomness of wind speed also leads to lowprediction accuracy for wind speed. According to this situation, this paper constructs the short-timeforecasting 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 theregression relationships between the historical data and forecasting data of wind speed. In order to improvethe forecast precision, historical data is clustered by cluster analysis so that the historical data whose changingtrend is similar with the forecasting data can be filtered out. The filtered historical data is used as the trainingsamples for SVR and the parameters would be optimized by particle swarm optimization (PSO). Theforecasting model is tested by actual data and the forecast precision is more accurate than the industrystandards. The results prove the feasibility and reliability of the model.
Abstract
There are many factors that affect the wind speed. In addition, the randomness of wind speed also leads to lowprediction accuracy for wind speed. According to this situation, this paper constructs the short-timeforecasting 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 theregression relationships between the historical data and forecasting data of wind speed. In order to improvethe forecast precision, historical data is clustered by cluster analysis so that the historical data whose changingtrend is similar with the forecasting data can be filtered out. The filtered historical data is used as the trainingsamples for SVR and the parameters would be optimized by particle swarm optimization (PSO). Theforecasting model is tested by actual data and the forecast precision is more accurate than the industrystandards. The results prove the feasibility and reliability of the model.
참고문헌 (19)
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