Impact on Predictive Performance of Air Pollutants in PV Forecasting Using Multi-Model Ensemble Learning: Evidence from the Port Logistics Hinterland Area

The uncertainty of photovoltaic (PV) power generation can impact the stability and flexibility of the power grid. Thus, accurately forecasting PV power output is crucial for ensuring a stable power system and supporting next-generation policy decisions. The purpose of this study is to examine how the PV power generation forecasting model performed both with and without the addition of particulate matter (PM) and greenhouse gas (GHG) concentration factors with meteorological data. In this study, PV power generation is forecasted by models based on various machine learning models. The results indicate that there was no significant difference in forecasting accuracy whether PM and GHG variables were included or not. In addition, the stacked ensemble model has the lowest root mean square error (RMSE) and mean absolute error (MAE) values for all datasets and shows improved performance compared to the single model. Stacked ensemble that include a combination of meteorological, PM, and GHG variables perform the best. However, the optimal datasets varied across models. Therefore, this study concluded that meteorological variables had the greatest influence on the PV generation forecasting performance. Among the additional factors, PM contributed more significantly to the improvement in forecasting performance than GHG.