To explore practical methods for forecasting regional distributed photovoltaic(PV) power generation and to improve the accuracy of such predictions

it proposes a day-ahead forecasting method that combines numerical weather prediction and historical statistical models.Firstly

it utilizes robotic process automation(RPA) technology and meteorological service platforms to automatically acquire photovoltaic generation data and weather forecast information.Then by analyzing the relationship between historical generation data and weather factors in various counties

it establishes a forecasting model.The model takes into account key influencing factors such as radiation temperature

humidity

and wind speed

and is developed and validated using regression analysis methods.Finally

the results show that this method can enhance the accuracy and reliability of predictions

with improvements compared to traditional methods.The automated data collection and processing procedures not only increase work efficiency but also reduce the possibility of human errors.The method has practical value to optimize the power generation plan and improves the utilization rate of renewable energy.