Enhancing wind power forecasting using hybrid multi-head attention and 1-dimensional convolutional neural networks

Rahman, Saifur, Shaher, Abdullah Khallufah M., Khan, Nabeel Ahmed, Abubakar, Muhammed, Mushtaq, Zohaib, Alwadie, Hatim, Hindi, Ayman Taher, Irfan, Muhammed and Al Dawsari, Saleh 2026. Enhancing wind power forecasting using hybrid multi-head attention and 1-dimensional convolutional neural networks. Revista Internacional de Métodos Numéricos para Cálculo y Diseño en Ingeniería 42 (1) , 32.

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Abstract

The accurate forecasting of wind power plays a veritable part in integrating renewable energy from wind turbines into power grids. Wind power, being a highly volatile mode of energy generation owing to temporal variations and complex weather patterns, renders reliable predictions essential for energy management and grid stability. In order to tackle this, we propose a hybrid Multi-Head Attention and 1D-Convolutional Neural Network (MHA-CNN) architecture that combines attention mechanisms and convolutional layers to capture both long-term dependencies and localized features in time-series data from a Supervisory Control and Data Acquisition (SCADA) system. The model effectively improves forecasting performance by attaining an R2score of 99.42 for hour-ahead and 96.52 for day-ahead predictions on a 50,540-sample, 10-min SCADA dataset using 5-fold chronological cross-validation, outperforming traditional methods without any manual feature engineering. The proposed method is also evaluated across multiple scenarios to assess the robustness of the proposed approach.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Engineering
Publisher:	Scipedia
Date of First Compliant Deposit:	3 February 2026
Date of Acceptance:	10 November 2025
Last Modified:	03 Feb 2026 16:26
URI:	https://orca.cardiff.ac.uk/id/eprint/184380

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