Wind speed prediction using a hybrid model of EEMD and LSTM considering seasonal features

• DOI: 10.1016/j.egyr.2022.07.007
• Publication Date: 2022-07-14T18:46:45Z
• Authors: Yi Yan; Xuerui Wang; Fei Ren; Zhuliang Shao; Chenlu Tian
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; SARIMA-EEMD-LSTM; Electrical engineering. Electronics. Nuclear engineering; 02 engineering and technology; Prediction accuracy; Time scales; Wind speed prediction; TK1-9971

As a clean and renewable energy source, wind power is of great significance for addressing global energy shortages and environmental pollution. However, the uncertainty of wind speed hinders the direct use of wind power, resulting in a high proportion of abandoned wind. Therefore, the accurate prediction of wind speed is of great significance in improving the utilization rate of wind energy. In this study, a hybrid wind speed prediction model is proposed based on seasonal autoregressive integrated moving average (SARIMA), ensemble empirical mode decomposition (EEMD), and long short-term memory (LSTM) methods. First, the original wind speed data were resampled to obtain wind speed data within time scales of 15, 30, and 60 min. The SARIMA model was used to extract the linear features and nonlinear residual sequences of wind speed time series at different time scales, and EEMD was used to decompose the nonlinear residual sequence to obtain intrinsic mode functions (IMFs) and sub-residual sequences. For the IMFs and sub-residual sequence obtained after decomposition, the LSTM method was used for training, and the predicted IMFs, sub-residual sequence, seasonal time series, and linear time series were integrated to obtain the final prediction wind speed. To verify the superiority of the wind speed hybrid prediction model proposed in this study, the original wind speed data of a large wind farm were used as a case study. Finally, the proposed wind speed hybrid prediction model was compared with other prediction models, verifying that this experimental model has a higher prediction accuracy.