Wind speed forecasting using optimized bidirectional LSTM based on dipper throated and genetic optimization algorithms - Alhussan, Amel Ali; M. El-Kenawy, El-Sayed; Abdelhamid, Abdelaziz A.; Ibrahim, Abdelhameed; Eid, Marwa M.; Khafaga, Doaa Sami (Frontiers in Energy Research, 2023)
Accurate forecasting of wind speed is crucial for power systems' stability. Many machine learning models have been developed for forecasting wind speed with reasonable accuracy. However, the accuracy of these models still needs more improvements to achieve more accurate results. In this paper, an optimized model is proposed for boosting the accuracy of the prediction accuracy of wind speed. The optimization is performed in terms of a new optimization algorithm based on dipper-throated optimization (DTO) and genetic algorithm (GA), which is referred to as (GADTO). The proposed optimization algorithm is used to optimize the long short-term memory (LSTM) forecasting model parameters. To verify the effectiveness of the proposed methodology, a benchmark dataset freely available on Kaggle is employed in the conducted experiments. The dataset is first preprocessed to be prepared for further processing. In addition, feature selection is applied to select the significant features in the dataset using a new feature selection method based on the proposed GADTO algorithm. The selected features are used to learn the optimization algorithm to select the best configuration of the LSTM forecasting model. The optimized LSTM is used to predict the future values of the wind speed, and the results are analyzed using a set of evaluation criteria. In addition, a statistical test is performed to study the difference of the proposed approach compared to other approaches. The results of these tests confirmed the proposed approach’s statistical difference and its robustness in forecasting the wind speed with a root mean square error (RMSE) value of 0.012 that outperforms the performance of other methods included in the conducted experiments.