ArticleScientific reports2024
Hybrid attention-based deep neural networks for short-term wind power forecasting using meteorological data in desert regions.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Enhanced Dhole Optimization Algorithm for hyperparameter tuning of a TCN-BiGRU-MHA hybrid architecture for wind power forecasting.Scientific reports · 2026Article
- A multi dataset validation model for hybrid feature selection in wind energy maximum power point tracking systems.Scientific reports · 2026Article
- A new automatic forecasting method based on explainable deep dendritic artificial neural network.Scientific reports · 2025Article
- Sustainable blue economy: energy consumption and carbon‑neutrality strategies for Albania's marine and fisheries sectors.Carbon balance and management · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
This study introduces an optimized hybrid deep learning approach that leverages meteorological data to improve short-term wind energy forecasting in desert regions. Over a year, various machine learning and deep learning models have been tested across different wind speed categories, with multiple performance metrics used for evaluation. Hyperparameter optimization for the LSTM and Conv-Dual Attention Long Short-Term Memory (Conv-DA-LSTM) architectures was performed. A comparison of the techniques indicates that the deep learning methods consistently outperform the classical techniques, with Conv-DA-LSTM yielding the best overall performance with a clear margin. This method obtained the lowest error rates (RMSE: 71.866) and the highest level of accuracy (R
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