Combining simulate anneal algorithm with support vector regression to forecast wind speed

Accurate wind speed forecasting is essential for predicting the wind power output. The wind speed is randomness, so the forecasting is very difficult. Least squares support vector machines (LSSVM) for load forecasting requires the identification of relevant parameters by expert experiment, this paper proposed a combination of adaptive particle swarm optimization the relevant parameters of least square support vector machine to forecast the wind speed. Compare using the default parameters of LSSVM method, the experimental results show that the proposed method can effectively select the parameters and the proposed method has more accurate results than the default parameters LSSVM method.