A Fuzzy Seasonal Long Short-Term Memory Network for Wind Power Forecasting

Chin-Wen Liao, I-Chi Wang, Kuo-Ping Lin and Yu-Ju Lin
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Chin-Wen Liao: Department of Industrial Education and Technology, National Changhua University of Education, Changhua 50007, Taiwan
I-Chi Wang: Department of Industrial Education and Technology, National Changhua University of Education, Changhua 50007, Taiwan
Kuo-Ping Lin: Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 40704, Taiwan
Yu-Ju Lin: Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 40704, Taiwan

Mathematics, 2021, vol. 9, issue 11, 1-15

Abstract: To protect the environment and achieve the Sustainable Development Goals (SDGs), reducing greenhouse gas emissions has been actively promoted by global governments. Thus, clean energy, such as wind power, has become a very important topic among global governments. However, accurately forecasting wind power output is not a straightforward task. The present study attempts to develop a fuzzy seasonal long short-term memory network (FSLSTM) that includes the fuzzy decomposition method and long short-term memory network (LSTM) to forecast a monthly wind power output dataset. LSTM technology has been successfully applied to forecasting problems, especially time series problems. This study first adopts the fuzzy seasonal index into the fuzzy LSTM model, which effectively extends the traditional LSTM technology. The FSLSTM, LSTM, autoregressive integrated moving average (ARIMA), generalized regression neural network (GRNN), back propagation neural network (BPNN), least square support vector regression (LSSVR), and seasonal autoregressive integrated moving average (SARIMA) models are then used to forecast monthly wind power output datasets in Taiwan. The empirical results indicate that FSLSTM can obtain better performance in terms of forecasting accuracy than the other methods. Therefore, FSLSTM can efficiently provide credible prediction values for Taiwan’s wind power output datasets.

Keywords: fuzzy seasonal; LSTM; wind power (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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