Enhanced Dhole Optimization Algorithm for hyperparameter tuning of a TCN-BiGRU-MHA hybrid architecture for wind power forecasting

) = 0.9888, and Weighted Absolute Percentage Error (WAPE) = 5.93%, surpassing all literature studies using the same dataset. With its excellent generalization capability and low overfitting tendency, the model offers reliable potential for use in real-time energy management systems.

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Publication Details

Journal
Scientific Reports
Published
2026-04-21
DOI
https://doi.org/10.1038/s41598-026-45657-0
Primary Topic
Energy Load and Power Forecasting
Type
article
Field-Weighted Citation Impact
0.00
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article

Enhanced Dhole Optimization Algorithm for hyperparameter tuning of a TCN-BiGRU-MHA hybrid architecture for wind power forecasting

Timur Lale
Scientific Reports
Energy Load and Power Forecasting
article

Enhanced Dhole Optimization Algorithm for hyperparameter tuning of a TCN-BiGRU-MHA hybrid architecture for wind power forecasting

Timur Lale
article en

Abstract

) = 0.9888, and Weighted Absolute Percentage Error (WAPE) = 5.93%, surpassing all literature studies using the same dataset. With its excellent generalization capability and low overfitting tendency, the model offers reliable potential for use in real-time energy management systems.

Scientific Reports
Batman University (TR)
Affordable and clean energy
Openalex Percentile: Top 11%
Energy Load and Power Forecasting
DOIOpen Access PDFOpenAlex
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Enhanced Dhole Optimization Algorithm for hyperparameter tuning of a TCN-BiGRU-MHA hybrid architecture for wind power forecasting — Timur Lale · Scientific Reports (2026) | TGRS Research Map | TGRS