Multi-Scale Residual Network With Modified Coordinate Attention for Accurate Short-Term PV Power Forecasting

Abstract

Accurate photovoltaic (PV) power forecasting is crucial for reliable grid operation, energy management, and the effective integration of PV systems into modern power grids. In this study, a short-term PV power forecasting framework is designed based on a residual multi-scale network enhanced with a modified coordinate attention module (MCAM). The active PV power is decomposed into intrinsic mode functions (IMFs) using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the obtained components are combined with radiation, temperature, and wind speed variables. The feature matrix is transformed into a multi-channel tensor through a sliding-window-based representation, enabling the network to learn feature–time dependencies in a structured form. The proposed architecture includes three residual multi-scale stages with cascaded convolutional branches to extract local and contextual patterns from different kernels. The MCAM is integrated after each multi-scale block to emphasize the spatio–temporal features while suppressing less relevant features. Experiments are conducted on real-world PV data from the Yulara solar power system for the 1-h, 2-h, and 3-h forecasting horizons. The proposed model achieves R2 values of 0.9950, 0.9902, and 0.9836, with RMSE values of 0.4400, 0.6185, and 0.7994 for 1-h, 2-h, and 3-h horizons, respectively. The effectiveness of the proposed method is validated through comprehensive comparisons with benchmark models and evaluations under seasonal and weather conditions.