Ultra-short-term wind power forecasting based on feature weight analysis and cluster dynamic division

Accurate and reliable ultra-short-term wind power forecasting (WPF) is of great significance to the safe and stable operation of power systems, but the current research is difficult to balance the prediction accuracy, timeliness, and applicability at the same time. Therefore, this paper proposes a ultra-short-term WPF model based on feature weight analysis and cluster dynamic division. The model introduces an analytic hierarchy process and an entropy weight method to analyze the subjective and objective weight of the influencing features of wind power, respectively, then the subjective and objective weight ratio is determined by the quantum particle swarm optimization (QPSO) algorithm to obtain a more reasonable comprehensive weight of each feature. On this basis, it uses the K-Medoids algorithm to dynamically divide the wind power clusters into class regions by cycles. Then, the class region is used as the prediction unit to establish the TCN-BiLSTM model based on temporal convolutional networks (TCN) and bi-directional long short-term memory (BiLSTM) for training and prediction and optimizes the hyper-parameters of the model by the QPSO algorithm. Finally, the regional predictions are summed to obtain the final ultra-short-term power prediction. In addition, in order to verify the performance of the model, the actual operation data of a power field in Xinjiang, China, are selected for the example validation. The results show that the proposed model can ensure the prediction accuracy while minimizing the training time of the model and outperforms other existing methods in terms of prediction accuracy, timeliness, and applicability.