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 pape...

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Bibliographic Details
Published in2010 Second IITA International Conference on Geoscience and Remote Sensing Vol. 2; pp. 92 - 94
Main Authors Tang Hui, Niu Dongxiao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2010
Subjects
adaptive optimization
Annealing
Forecasting
Least squares support vector machines
particle swarm optimization
Predictive models
simulate anneal
Simulated annealing
Support vector machines
Wind forecasting
Wind speed
wind speed forecasting
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Summary: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.
ISBN:9781424485147
1424485142
DOI:10.1109/IITA-GRS.2010.5603274