A deep learning model based on multi-attention mechanism and gated recurrent unit network for photovoltaic power forecasting

作     者：Yang, Kuo Cai, Yanjie Cheng, Jinrun 

作者机构：Shanghai Dianji Univ Sch Mech Engn Shanghai 200444 Peoples R China 

出 版 物：《COMPUTERS & ELECTRICAL ENGINEERING》 (Comput Electr Eng)

年 卷 期：2025年第123卷

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Shanghai Science and Technology Innovation Action Plan Morning Star Project (Sail Special) [24YF2714500] 

主　　题：Multi-attention mechanism Gated recurrent unit Encoder-decoder Photovoltaic power prediction 

摘      要：Solar energy plays a crucial role in the power grid due to its clean, stable, and cost-effective nature, as well as its significant storage potential. Accurate short-term photovoltaic (PV) power forecasting is essential for effective grid management and dispatching decisions. This study introduces a hybrid deep learning model integrating multiple attention mechanisms and gated recurrent unit networks to forecast PV output power one day in advance. To address the impact of random weather variations and historical PV power data on forecasting accuracy, the model incorporates an input attention mechanism to process input features. Additionally, temporal and spatial attention mechanisms are embedded within the encoder-decoder framework to enhance prediction performance. These mechanisms effectively capture the relationships between historical PV power output and meteorological variables while identifying crucial time-dependent hidden states. The proposed model is validated on a real-world PV dataset, achieving a mean absolute error of 0.0903 under favorable weather conditions, demonstrating a 22.5 % improvement over traditional forecasting methods across various weather classifications. Comparative analyses with other state-of-the-art models confirm that the proposed approach offers superior predictive accuracy.