Chaotic time series wind power interval prediction based on quadratic decomposition and intelligent optimization algorithm

Wind power prediction plays a critical role in the economic deployment of power systems including wind farm. To improve the prediction accuracy of wind power, a chaotic wind power interval prediction method, which relies on quadratic decomposition and improved state transition algorithm, is proposed in this study. Firstly, the data are decomposed twice through the optimal variational mode decomposition, improved complete ensemble empirical mode decomposition and permutation entropy (OVMD-ICEEMDAN-PE) in combination, so as to deal with the volatility and complexity of wind power time series. Then, the decomposed data are used to build the prediction model for each component. In addition, the improved state transition algorithm is introduced to optimize the hyperparameters of the convolutional neural network and bidirectional long short-term memory (CNN-BiLSTM), time convolutional network (TCN) and gated recurrent unit (GRU) of the prediction models. With the optimal hyperparameters solved for each model, the linear weights are combined to achieve accurate prediction. Finally, through the combined prediction model, interval prediction is achieved. The results show that the proposed method outperforms other models in the accuracy of prediction.