A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model

Bingchun Liu, Shijie Zhao, Xiaogang Yu, Lei Zhang and Qingshan Wang
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Bingchun Liu: Research Institute of Circular Economy, Tianjin University of Technology, Tianjin 300384, China
Shijie Zhao: Research Institute of Circular Economy, Tianjin University of Technology, Tianjin 300384, China
Xiaogang Yu: Research Institute of Circular Economy, Tianjin University of Technology, Tianjin 300384, China
Lei Zhang: Research Institute of Circular Economy, Tianjin University of Technology, Tianjin 300384, China
Qingshan Wang: School of Humanities, Tianjin Agricultural University, Tianjin 300384, China

Energies, 2020, vol. 13, issue 18, 1-17

Abstract: Wind power generation is one of the renewable energy generation methods which maintains good momentum of development at present. However, its extremely intense intermittences and uncertainties bring great challenges to wind power integration and the stable operation of wind power grids. To achieve accurate prediction of wind power generation in China, a hybrid prediction model based on the combination of Wavelet Decomposition (WD) and Long Short-Term Memory neural network (LSTM) is constructed. Firstly, the nonstationary time series is decomposed into multidimensional components by WD, which can effectively reduce the volatility of the original time series and make them more stable and predictable. Then, the components of the original time series after WD are used as input variables of LSTM to predict the national wind power generation. Forty points were used, 80% as training samples and 20% as testing samples. The experimental results show that the MAPE of WD-LSTM is 5.831, performing better than other models in predicting wind power generation in China. In addition, the WD-LSTM model was used to predict the wind power generation in China under different development trends in the next two years.

Keywords: wind power generation; hybrid prediction model; wavelet decomposition; long short-term memory; scenario analysis (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (38)

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