An hourly solar radiation prediction model using eXtreme gradient boosting algorithm with the effect of fog-haze

Hourly global solar radiation data is an important factor for solar energy utilization. Due to the lack of solar radiation observation stations in many areas, some hourly solar radiation models are proposed to predict hourly solar radiation. However, the existing models perform poorly in heavy fog-haze areas because the weakening effect of fog-haze on solar radiation is not considered. Thus, in this paper, hourly global solar radiation prediction models are developed considering air quality index (AQI) using XGBoost algorithm. The results show a general improvement in the accuracy of models with AQI as an additional input (Model B1-B6) compared to models that do not consider AQI (Model A1-A6). Compared to Model A, Model B have an increase in R value from 0.927 to 0.948, a decrease in RMSE value from 0.300 to 0.282 and a decrease in MAPE value from 0.159 to 0.145. In addition, for hourly solar radiation prediction, the six most important inputs are the day of the year, air temperature difference, surface temperature difference, hour, AQI, and total cloud cover.