Short-Term Wind Speed Forecast Based on Least Squares Support Vector Machine

Yanling Wang, Xing Zhou, Likai Liang, Mingjun Zhang, Qiang Zhang and Zhiqiang Niu
Online First Paper
10.3745/JIPS.04.0069
Keywords: Cluster Analysis, Least Squares, Least Squares Support Vector Regression (LSSVR), Particle Swarm Optimization (PSO), Short-Time Wind Speed Forecasting
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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.

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Cite this article
IEEE Style
Yanling Wang, Xing Zhou, Likai Liang, Mingjun Zhang, Qiang Zhang, and Zhiqiang Niu, "Short-Term Wind Speed Forecast Based on Least Squares Support Vector Machine," Journal of Information Processing Systems. DOI: 10.3745/JIPS.04.0069.

ACM Style
Yanling Wang, Xing Zhou, Likai Liang, Mingjun Zhang, Qiang Zhang, and Zhiqiang Niu, "Short-Term Wind Speed Forecast Based on Least Squares Support Vector Machine," Journal of Information Processing Systems, DOI: 10.3745/JIPS.04.0069.