Wind speed forecasting using optimized bidirectional LSTM based on dipper throated and genetic optimization algorithms

Accurate forecasting of wind speed is crucial for power systems stability. Many machine learning models have been developed to forecast accurately. However, the accuracy these still needs more improvements achieve accurate results. In this paper, an optimized model proposed boosting prediction speed. The optimization performed in terms a new algorithm based on dipper-throated (DTO) and genetic (GA), which referred as (GADTO). used optimize bidrectional long short-term memory (BiLSTM) parameters. To verify effectiveness methodology, benchmark dataset freely available Kaggle employed conducted experiments. first preprocessed be prepared further processing. addition, feature selection applied select significant features using binary version GADTO algorithm. selected are utilized learn best configuration BiLSTM model. predict future values speed, resulting predictions analyzed set evaluation criteria. Moreover, statistical test study difference approach compared other approaches analysis variance (ANOVA) Wilcoxon signed-rank tests. results tests confirmed approach’s its robustness with average root mean square error (RMSE) 0.00046, outperforms performance recent methods.