Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm

It is hard to predict wind power with high-precision due to its non-stationary and stochastic nature. The wind power has developed rapidly around the world as a promising renewable energy industry. The uncertainty of wind power brings difficult challenges to the operation of the power system with th...

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Bibliographic Details
Published in:Journal of cleaner production 2020-01, Vol.242, p.118447, Article 118447
Main Authors: Li, Ling-Ling, Zhao, Xue, Tseng, Ming-Lang, Tan, Raymond R.
Format: Article
Language:English
Subjects:
Differential evolution
Improved dragonfly algorithm
Prediction accuracy
Support vector machine
Wind power prediction
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Summary:It is hard to predict wind power with high-precision due to its non-stationary and stochastic nature. The wind power has developed rapidly around the world as a promising renewable energy industry. The uncertainty of wind power brings difficult challenges to the operation of the power system with the integration of wind farms into power grid. Accurate wind power prediction is increasingly important for the stable operation of wind farms and the power grid. This study is combined support vector machine and improved dragonfly algorithm to forecast short-term wind power for a hybrid prediction model. The adaptive learning factor and differential evolution strategy are introduced to improve the performance of traditional dragonfly algorithm. The improved dragonfly algorithm is used to choose the optimal parameters of support vector machine. The effectiveness of the proposed model has been confirmed on the real datasets derived from La Haute Borne wind farm in France. The proposed model has shown better prediction performance compared with the other models such as back propagation neural network and Gaussian process regression. The proposed model is suitable for short-term wind power prediction. The flowchart of improved dragonfly algorithm. [Display omitted] •An IDA-SVM model is proposed for short-term wind power forecasting.•The learning factor and differential evolution strategy are introduced in IDA.•The IDA is used to optimize the parameters of SVM.•The predicted results show that the IDA-SVM has better prediction accuracy.
ISSN:0959-6526
1879-1786
DOI:10.1016/j.jclepro.2019.118447