An ultra‐short‐term wind speed prediction model using LSTM based on modified tuna swarm optimization and successive variational mode decomposition
Wumaier Tuerxun, Chang Xu, Hongyu Guo, Lei Guo, Namei Zeng, Zhiming Cheng
Abstract
Abstract Accurate ultra‐short‐term wind speed prediction is extremely important for the power control of wind farms, the safe dispatch of power systems, and the stable operation of power grids. At present, most wind farms mainly rely on supervisory control and data acquisition systems to obtain operation and maintenance data which includes operating characteristics of wind turbines. In the ultra‐short‐term wind speed prediction, a long short‐term memory network is one of the commonly used deep learning methods. To address the problem that improper selection of long short‐term memory network's hyperparameters may affect the prediction results, In the present study, a hybrid prediction model based on the long short‐term memory and the modified tuna swarm optimization algorithm was established, and was used to predict after the wind speed sample data had been decomposed by successive variational mode decomposition method. The experimental results reveal that the proposed model effectively improved the accuracy of wind speed prediction for wind farms compared with the support vector regression, deep belief networks, and long short‐term memory models optimized by particle swarm optimization algorithm.