A hybrid deep learning framework for day-ahead PV forecasting using coarse-grained weather data

Accurate day-ahead photovoltaic forecasting is critical for grid reliability and market operations, especially when high-resolution weather data are unavailable. Existing models often suffer reduced accuracy or data leakage risks when handling coarse-grained meteorological inputs. This work proposes a novel deep learning framework that enhances photovoltaic forecasting performance using low-resolution weather data while explicitly avoiding information leakage during decomposition. The proposed method combines three key components: (1) a Similar-Day Reconstruction strategy that identifies and ranks past weather-photovoltaic profiles using multi-segment weighted similarity matching; (2) a dual-branch prediction module that separates trend and volatility modelling, where Multi-variable Variational Mode Decomposition is used for trend extraction in a leakage-free manner; and (3) a Multi-Stream Learning Convolutional Network that fuses Long Short-Term Memory networks, Convolutional Neural Networks, residual, and attention pathways to capture multi-scale temporal features. Final forecasts are generated through weighted integration of both branches. Extensive experiments on three photovoltaic datasets demonstrate that the proposed approach outperforms benchmark models (e.g., Long Short-Term Memory networks, Transformer) across multiple weather conditions. It maintains robust prediction accuracy despite low-granularity weather inputs. The framework demonstrates strong generalization, methodological rigor in avoiding information leakage, and practical value for real-world photovoltaic forecasting applications where high-resolution weather data may be unavailable.