A physical model with meteorological forecasting for hourly rooftop photovoltaic power prediction

Accurate photovoltaic power forecasting provides essential information for the flexible control of building energy systems. This paper proposes a physical model with environmental parameter prediction and an improved maximum power point tracking algorithm for hourly photovoltaic power forecasting. This study incorporates the forecast of meteorological parameters into the photovoltaic model so that the physical model proposed in this paper can achieve photovoltaic power prediction in the face of different weather conditions compared to previous photovoltaic physical models in the literature. A hierarchical clustering algorithm was used to obtain future hourly irradiance based on different weather conditions. The coordinate analysis method was used to calculate the hourly irradiance received on the surface of the photovoltaic panel. An equivalent circuit model was used to calculate the current-voltage characteristics of photovoltaic panels. Finally, the output voltage of the photovoltaic panel was adjusted by the improved maximum power point tracking algorithm to obtain the photovoltaic power. This algorithm can accelerate the calculation process and avoid long convergence times or oscillations near the optimal value. A photovoltaic project located in central China was selected as a case study to verify the accuracy of the prediction model. The long-term monitoring results show that the relative error of the predicted irradiance of the photovoltaic panel surface is 18.5%. The mean absolute error of the forecasted photovoltaic power was 15.9% in 120 consecutive days under various weather conditions, indicating that the model had high accuracy compared with traditional machine learning methods.