A Forecasting Model of Wind Power Based on IPSO–LSTM and Classified Fusion

Qiuhong Huang and Xiao Wang
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Qiuhong Huang: Department of Electrical Engineering, Guizhou University, Guiyang 550025, China
Xiao Wang: Department of Electrical Engineering, Guizhou University, Guiyang 550025, China

Energies, 2022, vol. 15, issue 15, 1-19

Abstract: To improve the predicting accuracy of wind power, this paper proposes a forecasting model of wind power based on the IPSO–LSTM model and classified fusion, which not only overcomes the shortcoming of the artificially determined parameters of LSTM, but also solves the problem that the fused accuracy may be reduced by the environment when adopting a single fusion model. Firstly, some wind speed sub-series were obtained by decomposing the original wind speed according to the wavelet packet decomposition (WPD), and the data sets formed by combining these sub-series with meteorological elements. Subsequently, the wind power components formed by wind speed decomposition are predicted through the long short-term memory neural network (LSTM), which is optimized by the improved particle swarm optimization (IPSO). Consequently, the predicting value of the final wind power was acquired by adopting the method of classified fusion to calculate the wind power components. Several case studies were carried out on the proposed model with the help of Python. It is found from those relevant results that the RMSE and MAE of the proposed model is 1.2382 and 0.8210, respectively. Moreover, the R 2 is 0.9952. Those simulating results show that the proposed model may be better for fitting the actual curve of wind power and has excellent predicting accuracy.

Keywords: IPSO; LSTM; wind power forecast; classification of the fusion pattern; data fusion (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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