@article{Zhang2026, 
author = {Wei Zhang and Shengyi Li and Jiyuan Gao and Zhongsheng Liu and Hannan Zhang},
title = {Day-Ahead Photovoltaic Power Forecasting Based on Dual NWP Correction with Transitional Weather Classification},
year = {2026},
journal = {Power and Energy Future},
volume = {1},
number = {3},
pages = {9650021},
keywords = {NWP correction, day-ahead photovoltaic power forecasting, transitional weather classification, attention-BiGRU},
url = {https://www.sciopen.com/article/10.26599/PEF.2026.9650021},
doi = {10.26599/PEF.2026.9650021},
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.}
}