Solar Irradiance Prediction Method for PV Power Supply System of Mobile Sprinkler Machine Using WOA-XGBoost Model

Solar energy can mitigate the power supply shortage in remote regions for portable irrigation systems. The accurate prediction of solar irradiance is crucial for determining the power capacity of photovoltaic power generation (PVPG) systems for mobile sprinkler machines. In this study, a prediction method is proposed to estimate the solar irradiance of typical irrigation areas. The relation between meteorological parameters and solar irradiance is studied, and four different parameter combinations are formed and considered as inputs to the prediction model. Based on meteorological data provided by ten typical radiation stations uniformly distributed nationwide, an Extreme Gradient Boosting (XGBoost) model optimized using the Whale Optimization Algorithm (WOA) is developed to predict solar radiation. The prediction accuracy and stability of the proposed method are then evaluated for different input parameters through training and testing. The differences between the prediction performances of models trained based on single-station data and mixed data from multiple stations are also compared. The obtained results show that the proposed model achieves the highest prediction accuracy when the maximum temperature, minimum temperature, sunshine hours ratio, relative humidity, wind speed, and extraterrestrial radiation are used as input parameters. In the model testing, the RMSE and MAE of WOA-XGBoost are 2.142 MJ·m−2·d−1 and 1.531 MJ·m−2·d−1, respectively, while those of XGBoost are 2.298 MJ·m−2·d−1 and 1.598 MJ·m−2·d−1. The prediction effectiveness is also verified based on measured data. The WOA-XGBoost model has higher prediction accuracy than the XGBoost model. The model developed in this study can be applied to forecast solar irradiance in different regions. By inputting the meteorological parameter data specific to a given area, this model can effectively produce accurate solar irradiance predictions for that region. This study provides a foundation for the optimization of the configuration of PVPG systems for mobile sprinkler machines.