SHORT-TERM WIND SPEED PREDICTION BASED ON MULTI-OBJECTIVE OPTIMIZATION AND ERROR CORRECTION

Li Jiawen; Sheng Deren; Li Wei; Chen Jianhong

doi:10.19912/j.0254-0096.tynxb.2020-1312

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (8) : 273-280. DOI: 10.19912/j.0254-0096.tynxb.2020-1312

SHORT-TERM WIND SPEED PREDICTION BASED ON MULTI-OBJECTIVE OPTIMIZATION AND ERROR CORRECTION

Li Jiawen, Sheng Deren, Li Wei, Chen Jianhong

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Abstract

This paper presents a multi-objective optimization and error correction model for short-term wind speed prediction. Firstly, decompose the original wind speed data to reduce the nonlinearity of the sequence. An effective multi-objective optimization algorithm is used to optimize the ELM neural network, which ensures the accuracy and stability of the prediction. Finally, the deep learning network LSTM is used to correct the error of the initial prediction results. In order to overcome the difficulty of super parameter selection, crow algorithm is used to optimize the number of neurons in layer. An example is given to analyze the data of a wind farm in central China, and the results show that the method has high prediction accuracy.

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wind speed / prediction / multi-objective optimization / error correction / deep learning

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Li Jiawen, Sheng Deren, Li Wei, Chen Jianhong. SHORT-TERM WIND SPEED PREDICTION BASED ON MULTI-OBJECTIVE OPTIMIZATION AND ERROR CORRECTION[J]. Acta Energiae Solaris Sinica. 2022, 43(8): 273-280 https://doi.org/10.19912/j.0254-0096.tynxb.2020-1312

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