MIPV-NWP-PINNs V1.0: development of a multi-scale photovoltaic power forecasting framework integrating numerical weather prediction with physics-informed neural networks

Abstract. Photovoltaic (PV) power generation has become a cornerstone of clean energy, for which accurate forecasting is essential to ensure safe and efficient grid integration. However, raw Numerical Weather Prediction (NWP) outputs often fail to provide reliable forecasts because PV power is influenced by multiple coupled factors, including meteorological factors and photovoltaic modules. To address this challenge, this study develops a multi-scale PV power forecasting framework that integrates NWP with deep learning techniques (MIPV-NWP-PINN) and evaluates its performance using PV module monitoring data from a power station in northwestern China. First, a regional high-resolution NWP system based on the Weather Research and Forecasting (WRF) model is established to generate multi-scale meteorological forecasts with lead times of 6 hours, 1 day, 3 days, and 5 days. Next, a novel hybrid correction model that combines Quantile Mapping with a Temporal Pattern Attention-based Long Short-Term Memory (TPA-LSTM) network is proposed to improve the accuracy of Global Horizontal Irradiance (GHI) forecasts. This correction approach reduces the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) by more than 23 % compared to raw NWP outputs. Building on these corrected meteorological forecasts, a Physics-Informed Neural Network (PINN)-iTransformer model is developed for the final prediction of PV power. By incorporating physical constraints directly into its loss function, this model consistently outperforms state-of-the-art alternatives across all forecasting horizons, achieving reductions of 15.5 % in RMSE and 12.4 % in MAE. This physics-constrained framework substantially improves the accuracy and robustness of PV power forecasting across multiple time scales. The enhanced reliability directly supports secure PV grid integration and contributes to the broader transition toward low-carbon energy systems.