Forecasting model for short-term wind speed using robust local mean decomposition, deep neural networks, intelligent algorithm, and error correction - Li, Jiawen; Liu, Minghao; Wen, Lei (Frontiers in Energy Research, 2024)
Wind power generation arouse widely concerned by the world.Accurate prediction of wind speed is very important for the safe and economic operation of power grid. This paper presents a short-term wind speed prediction model which includes data decomposition, deep learning, in-telligent algorithm optimization and error correction modules. Firstly, the robust local mean decomposition (RLMD) is applied to the original wind speed data to reduce the non-stationarity of the data. Then, the salp swarm algorithm (SSA) is used to determine the optimal parameter combination of bidirectional gated recurrent unit (BiGRU) to ensure the prediction quality. In order to eliminate the predictable components of the error further, an correction module based on the improved salp swarm algorithm (ISSA) and deep extreme learning machine (DELM) is constructed. The exploration and exploitation capability of the original salp swarm algorithm is enhanced by introducing crazy operator and dynamic learning strategy, and the input weights and thresholds in DELM are optimized by ISSA to improve the generalization ability of the model. The actual data of wind farm are utilized to verify the advancement of the proposed model. Comparing with other models, the results show that the proposed model has the best prediction performance. As a powerful tool, the developed forecasting system is expected to be further utilized in the energy system.