Reducing data requirement for accurate photovoltaic power prediction using hybrid machine learning-physical model on diverse dataset

This research aims to develop a hybrid model combining machine learning and a physical model and demonstrate its effectiveness across diverse datasets, data scarcity scenarios, and not standard conditions. The hybridization approach involves parallel integration of physical and machine learning models, where the outputs of both models are weighted and yield improved predictions by assigning weight based on error minimization. A deep neural network serves as the machine learning model, while the single-diode model is employed as the physical model. This study utilizes six different solar PV for constructing the diverse dataset. Key findings reveal that the hybrid model can accurately predict power generation from a diverse range of solar PV, during data scarcity scenarios, and in all conditions. The model performs well in predicting solar PV power output in 6 types of solar PV with an average RMSE of 0.177 kW. The hybrid model outperforms machine learning and physical models by 1.5 % and 42 % respectively when trained on the full dataset. Moreover, when 97 % of the data is discarded intentionally, the hybrid model outperforms both machine learning and physical models by 47.7 % and 10.5 % respectively. The model also shows that it can improve the accuracy of the prediction during standard (solar irradiance = 1000 W m⁻²) and not standard conditions. The successful hybridization of machine learning and physical models addresses previously identified limitations in individual models. In addition, the model indicates a substantial improvement in terms of accuracy compared to prior published studies.