Day-Ahead Photovoltaic Power Forecasting Based on Dual NWP Correction with Transitional Weather Classification

Abstract

Numerical weather prediction (NWP) global horizontal irradiance (GHI) suffers from inherent biases that are drastically amplified under transitional weather, severely compromising day-ahead photovoltaic (PV) power forecasting accuracy. To address this issue, this study proposes a day-ahead PV forecasting method based on dual-stage NWP GHI correction with transitional weather classification. First, a dual-sliding-window approach identifies GHI turning points and categorizes weather-transition trends. Second, trend-specific attention-bidirectional gated recurrent unit (BiGRU) models perform the first-stage correction of NWP GHI. The residual errors are then decomposed via the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) into multiscale components, each corrected by a second-stage sttention-BiGRU to compensate for frequency-band errors. Finally, the dual-stage-corrected GHI serves as a key input for accurate day-ahead PV power forecasting. Case studies using operational data from Hebei, China show that the proposed method reduces mean absolute error (MAE) by up to 39% and achieves an approximately 10% relative improvement in R2 compared with raw NWP data at station 1 for day-ahead forecasting. These improvements are particularly pronounced under transitional weather, demonstrating the method’s effectiveness in enhancing forecast reliability and supporting grid dispatch and renewable energy integration.