Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression

Zhendong Zhang, Lei Ye, Hui Qin, Yongqi Liu, Chao Wang, Xiang Yu, Xingli Yin and Jie Li

Applied Energy, 2019, vol. 247, issue C, 270-284

Abstract: Wind energy has received more and more attention around the world since it is a kind of clean, economical and renewable energy. However, the strong randomness of the wind speed makes wind power difficult to integrate into the power grid. Obtaining reliable high-quality wind speed prediction results is very important for the planning and application of wind energy. In this study, Shared Weight Long Short-Term Memory Network (SWLSTM) is proposed to decrease the number of variables that need to be optimized and the training time of Long Short-Term Memory Network (LSTM) without significantly reducing prediction accuracy. Furthermore, a new hybrid model combined SWLSTM and GPR, called SWLSTM-GPR, is proposed to obtain reliable wind speed probabilistic prediction result. SWLSTM-GPR is applied to four wind speed prediction cases in Inner Mongolia, China and compared with the state-of-the-art wind speed prediction methods from four aspects: point prediction accuracy, interval prediction suitability, probability prediction comprehensive performance and training time. The reliability test of SWLSTM-GPR guarantees that the prediction results are reliable and convincing. The experimental results show that SWLSTM-GPR can obtain high-precision point prediction, appropriate prediction interval and reliable probabilistic prediction results with shorter training time on the wind speed prediction problems.

Keywords: Wind speed prediction; Long Short-Term Memory Network; Gaussian Process Regression; Shared weight; Forecast uncertainty (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (45)

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DOI: 10.1016/j.apenergy.2019.04.047

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