Ultra Short-Term Wind Power Forecasting Based on Sparrow Search Algorithm Optimization Deep Extreme Learning Machine

Guoqing An, Ziyao Jiang, Libo Chen, Xin Cao, Zheng Li, Yuyang Zhao and Hexu Sun
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Guoqing An: School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
Ziyao Jiang: School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
Libo Chen: School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
Xin Cao: Hebei Construction & Investment Group New Energy Company Ltd., Shijiazhuang 050051, China
Zheng Li: School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
Yuyang Zhao: School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
Hexu Sun: School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China

Sustainability, 2021, vol. 13, issue 18, 1-18

Abstract: Improving the accuracy of wind power forecasting is an important measure to deal with the uncertainty and volatility of wind power. Wind speed and wind direction are the most important factors affecting the power generation of wind turbines. In this paper, we propose a wind power forecasting method that combines the sparrow search algorithm (SSA) with the deep extreme learning machine (DELM). Based on the DELM model, the length of the time series’ influence on the performance of the neural network is validated through the comparison of the forecast error indexes, and the optimal time series length of the wind power is determined. The sparrow search algorithm is used to optimize its parameters to solve the problem of random changes in model input weights and thresholds. The proposed SSA-DELM model is validated using the measured data of a certain wind turbine, and various forecasting indexes are compared with several current wind power forecasting methods. The experimental results show that the proposed model has better performance in ultra-short-term wind power forecasting, and its coefficient of determination ( R ²), mean absolute error (MAE), and root mean square error (RMSE) are 0.927, 69.803, and 115.446, respectively.

Keywords: ultra-short-term wind power forecasting; deep extreme learning machine; sparrow search algorithm (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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