Short-term wind speed forecasting using an optimized three-phase convolutional neural network fused with bidirectional long short-term memory network model

Wind energy is an environment friendly, low-carbon, and cost-effective renewable energy source. It is, however, difficult to integrate wind energy into a mixed energy grid due to its high volatility and intermittency. For wind energy conversion systems to be reliable and efficient, accurate wind speed (WS) forecasting is fundamental. This study cascades a convolutional neural network (CNN) with a bidirectional long short-term memory (BiLSTM) in order to obtain a model for hourly WS forecasting by utilizing several meteorological variables as model inputs to study their effects on predicted WS. For input selection, the mutation grey wolf optimizer (TMGWO) is used. For efficient optimization of CBiLSTM hyperparameters, a hybrid Bayesian Optimization and HyperBand (BOHB) algorithm is used. The combined usage of TMGWO, BOHB, and CBiLSTM leads to a three-phase hybrid model (i.e., 3P-CBiLSTM). The performance of 3P-CBiLSTM is benchmarked against the standalone and hybrid BiLSTMs, LSTMs, gradient boosting (GBRs), random forest (RFRs), and decision tree regressors (DTRs). The statistical analysis of forecasted WS reveals that the 3P-CBiLSTM is highly effective over the other benchmark forecasting methods. This objective model also registers the highest percentage of forecasted errors (≈ 53.4 – 81.8%) within the smallest error range ≤|0.25| ms−1 amongst all tested study sites. Despite the remarkable results achieved, the CBiLSTM model cannot be generally understood, so the eXplainable Artificial Intelligence (xAI) technique was used for explaining local and global model outputs, based on Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). Both of the xAI methods determined that the antecedent WS is the most significant predictor of the short-term WS forecasting. Therefore, we aver that the proposed model can be employed to help wind farm operators in making quality decisions in maximizing wind power integration into the grid with reduced intermittency.