A Short-Term Wind Power Forecasting Approach Based on Model Configuration Optimization via Prequential-Cross Cooperative Validation Estimation

Efficient utilization of sustainable energy is imperative for supporting the globally escalating electricity demand. Because the unstable wind energy makes the wind power access challenging for power systems, the wind power forecasting becomes the critical part of the power dispatch. In this paper, a short-term wind power forecasting approach based on model configuration optimization via prequential-cross cooperative validation estimation (PCCVE) is proposed. It enables the hybrid ANN including the convolutional neural network, bidirectional long short-term memory network, and multi-head attention mechanism (CNN-BiLSTM-MHA) to better construct the wind speed–power mapping relationship for improving forecasting performance. Firstly, the box-plot local detection–correction combining the spatial–temporal optimal-weighted fuzzy clustering and the sliding window connected box-plot is proposed to reasonably detect and correct local outlier wind speed points. It prevents CNN-BiLSTM-MHA from being interfered with local outlier wind speed points. Secondly, PCCVE based on the prequential-validation estimation and cross-validation estimation is proposed to more accurately give the estimated error of CNN-BiLSTM-MHA, thus better assisting the optimization of the values of CNN-BiLSTM-MHA’s hyperparameters. It enables CNN-BiLSTM-MHA to efficiently construct the wind speed–power mapping relationship. By comparing different approaches on the actual wind farm dataset, the effectiveness and advantages of the proposed approach are demonstrated.