Short-term wind speed forecasting using recurrent neural networks with error correction

Jikai Duan, Hongchao Zuo, Yulong Bai, Jizheng Duan, Mingheng Chang and Bolong Chen

Energy, 2021, vol. 217, issue C

Abstract: As a type of clean energy, wind energy has been effectively used in power systems. However, due to the influence of the atmospheric boundary layer, wind speed exhibits strong nonlinearity and nonstationarity. Therefore, the accurate and stable prediction of wind speed is highly important for the security of the power grid. To improve the forecasting accuracy, a novel hybrid forecasting system is proposed in this paper that includes effective data decomposition techniques, recurrent neural network prediction algorithms and error decomposition correction methods. In this system, a novel decomposition approach is used to first decompose the original wind speed series into a set of subseries, then it predicts the wind speed by recurrent neural network, and finally, it decomposes the error to correct the previously predicted wind speed. The effectiveness of the proposed model is verified using data from four different wind farms in China. The results show that the proposed hybrid system is superior to other single models and traditional models and realizes highly accurate prediction of wind speed. The proposed system may be a useful tool for smart grid operation and management.

Keywords: Wind speed forecasting; Recurrent neural network; ARIMA (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (48)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:217:y:2021:i:c:s0360544220325044

DOI: 10.1016/j.energy.2020.119397

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