Work

Simulation and Correction Study of Solar Irradiance in Guangdong Based on WRF-Solar and Random Forest

Year: 2026

Type: article

Abstract: To improve solar irradiance simulation accuracy for precise photovoltaic power forecasting, we developed a hybrid framework combining WRF-Solar numerical simulation and random forest (RF) machine learning for a PV plant in Guangdong, China. Weather conditions were objectively classified into clear, intermittent cloudy, and overcast using the Daily Variability Index (DVI) and Daily Clear-sky Index (DCI). We calibrated the WRF-Solar model’s microphysics and radiative transfer schemes via sensitivity tests to optimize overcast-sky performance, then applied RF correction to the simulated irradiance. Results show that RF correction significantly reduces simulation errors for intermittent and overcast conditions, while the original WRF-Solar outperforms the corrected results under clear skies due to RF overfitting.

Source: Energies

DOI: 10.3390/en19092077

Authors: Yuanhong He, Zheng Li, Fang Zhou, Zhiqiu Gao

Institution: Nanjing University of Information Science and Technology

Language: English

Topic: Solar Radiation and Photovoltaics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137