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 wind speed accurately. However, the accuracy of these models still needs more improvements to achieve more accurate results. In this paper, an optimized model is pr...

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Bibliographic Details
Published in:Frontiers in energy research Vol. 11
Main Authors: Alhussan, Amel Ali, M. El-Kenawy, El-Sayed, Abdelhamid, Abdelaziz A., Ibrahim, Abdelhameed, Eid, Marwa M., Khafaga, Doaa Sami
Format: Journal Article
Language:English
Published: Frontiers Media S.A 01.06.2023
Subjects:
bidirectional long short-term memory
dipper throated optimization
genetic algorithm
machine learning
metaheuristic optimization
wind speed forecasting
ISSN:2296-598X, 2296-598X
Online Access:Get full text
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Summary:Accurate forecasting of wind speed is crucial for power systems stability. Many machine learning models have been developed to forecast wind speed accurately. 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 bidrectional long short-term memory (BiLSTM) 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 the binary version of the proposed GADTO algorithm. The selected features are utilized to learn the optimization algorithm to select the best configuration of the BiLSTM forecasting model. The optimized BiLSTM is used to predict the future values of the wind speed, and the resulting predictions are analyzed using a set of evaluation criteria. Moreover, a statistical test is performed to study the statistical difference of the proposed approach compared to other approaches in terms of the analysis of variance (ANOVA) and Wilcoxon signed-rank tests. The results of these tests confirmed the proposed approach’s statistical difference and its robustness in forecasting the wind speed with an average root mean square error (RMSE) of 0.00046, which outperforms the performance of the other recent methods.
ISSN:2296-598X
2296-598X
DOI:10.3389/fenrg.2023.1172176