An investigation of photovoltaic power forecasting in buildings considering shadow effects: Modeling approach and SHAP analysis

The power generation of distributed photovoltaic (PV) systems often suffers interference due to shadows cast by surrounding buildings. To improve the accuracy of PV power forecasts, this paper presents a PV power prediction method that takes shadow effects into consideration. Firstly, a convenient PV shadow model was formulated for predicting the proportion of PV shaded (PPS), using theoretical derivation and a zoning shading judgment strategy. Subsequently, a PV power prediction method was proposed based on PV shadow forecasting and the convolutional deep neural network algorithm. Finally, this method was applied to a carport PV system in a building in Beijing, China, and SHAP analysis was utilized for the interpretation. The results show that the proposed method can automatically recognize shadow conditions, and significantly improve the predictive accuracy of PV power, reducing the MAE by 10.1 % and increasing the R2 value from 0.91 to 0.94. The ranking of feature importance to the PV power prediction model is as follows: solar radiation, hour, ambient temperature, PPS, and relative humidity. This study offers a feasible solution for predicting power generation of PV systems that are subject to shadow shading from buildings.