Quantile-transformed multi-attention residual framework (QT-MARF) for medium-term PV and wind power prediction

Abstract

The increasing generation of renewable electrical power, particularly from wind and solar sources, has significantly influenced the national energy and power transmission systems. However, accurate forecasting of wind and photovoltaic (PV) power remains challenging due to the stochastic and highly nonlinear nature of wind speed and solar irradiance. Traditional models often fail to produce accurate power forecasts. To address this challenge, this paper proposes a novel deep learning model based on the Quantile-Transformed Multi-Attention Residual Framework (QT-MARF). The proposed model is built on a Transformer architecture with Residual Net and Multi-Head Attention. QT-MARF utilizes sequential processing through gated residual networks, enabling the model to learn complex patterns and make accurate power forecasts. The model utilizes PV and wind data from Natal, Santa Vitoria, and the Chinese State Grid (CSG). Case studies are conducted to validate the estimation performance of the hybrid models. The proposed QT-MARF demonstrates promising results in terms of accuracy and efficiency, outperforming traditional models in metrics such as Mean Absolute Error (MAE), correlation coefficient (CC), Root Mean Squared Error (RMSE), and R-squared (R2). Comparative analysis with state-of-the-art techniques such as the Inception-embedded attention-based memory fully-connected network (IAMFN) model, CNN-GRU, CNN-LSTM, and RNN highlights the superiority of the proposed model. These findings suggest that the proposed model offers a promising solution for the challenging task of wind and PV power forecasting.