Probabilistic optimization based adaptive neural network for short-term wind power forecasting with climate uncertainty

•The K-means optimized Gaussian mixture model is proposed for data clustering.•The improved snake algorithm is introduced for the hyperparameter optimization of LSTM.•Improved Snake algorithm excels on unimodal/multimodal functions.•The improved snake optimization-long short-term memory model increases accuracy of wind power forecasting. Advanced wind power prediction technique plays an essential role in the stable operation of the grid with large-scale grid integration of wind power. Most research focuses on distance-based static classification where the subjective nature of initial center selection increases the uncertainty of the prediction. And the data classification on a daily basis neglects the potentially significant climate changes at smaller time scales. To address these issues, the improved snake optimization-long short-term memory (ISO-LSTM) model with Gaussian mixture model (GMM) clustering is proposed to forecast wind power from an adaptive perspective. By exploiting the merits of the probabilistic classification, the K-means optimized GMM clustering enables an appropriate feature modelling for substantial climate changes at smaller time scales. Then the ISO algorithm exhibits higher search accuracy and is better suited for finding hyperparameter combinations for LSTM neural networks. The data from the National Aeronautics and Space Administration (NASA) of the US is used to validate the effectiveness of the proposed method. Compared to the traditional K-means clustering, the K-means optimized GMM clustering has increased accuracy by 2.63 %. Simultaneously, with the adoption of the enhanced ISO algorithm, the accuracy further increases by 7.27 %. Different existing models have also been tested; it shows that the proposed model demonstrates higher prediction accuracy.