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• Five data availability scenarios are compared for different physical model chains. • Nominal power and module orientation are the most critical design parameters. • Unknown tilt and azimuth angles and row spacing should be estimated. • Absolute error is more sensitive to missing design parameters than squared error. • Physical power calculation is encouraged even if some design parameters are unknown. Accurate photovoltaic (PV) power forecasts are essential for the grid integration of the technology. Most solar forecasting studies deal only with solar irradiance forecasting, which highlights the importance of the irradiance to power conversion methods to enable the practical utilization of the advanced irradiance forecasting techniques. The PV power output is commonly calculated with physical model chains that rely on the main design parameters of the PV plants. This study presents an analysis of how the unavailability or uncertain knowledge of the design parameters affects the physical power forecast accuracy. The analysis is performed by verifying the power forecasts created by the bests of 32,400 model chains for 16 PV plants in Hungary for five data availability scenarios based on numerical weather prediction data. The results show that even though the knowledge of all relevant design parameters ensures the lowers errors, the critical parameters are only the nameplate capacities and the module orientation. The increase in mean absolute error and root mean square error is only 1.3% and 0.5%, respectively, if only these crucial parameters are known compared to the baseline scenario. If necessary, the tilt and azimuth angles can also be inferred, where the resulting inaccuracy depends on the estimation errors. The results reveal that the physical model chains can be used with a decent accuracy for power forecast calculations even if the design parameters are unknown, which highly broadens the applicability of the physical method. [ABSTRACT FROM AUTHOR]

Irradiance-to-power conversion is an essential step of state-of-the-art photovoltaic (PV) power forecasting, regardless of the source and post-processing of irradiance forecasts. The two distinct approaches for mapping the irradiance forecasts to PV power are physical and data-driven, which can also be hybridized. The contribution of this paper is twofold; first, it proposes a concept and identifies the best implementation of a hybrid physical and machine learning irradiance-to-power conversion method. Second, a head-to-head comparison of the physical, data-driven, and hybrid methods is performed for the operational day-ahead power forecasting of 14 PV plants in Hungary based on numerical weather prediction (NWP). To respect the rule of consistency but still obtain as complete picture as possible, two directives are set, namely minimizing the mean absolute error (MAE) and minimizing the root mean square error (RMSE), and separate sets of forecasts are optimized for both directives. The results reveal that for two years of training data, the hybrid method that involves the most physically-calculated predictors can reduce the MAE by 5.2% and 10.4% compared, respectively, to the optimized physical model chains and the machine learning without any physical considerations. The two most important physical modeling steps are separation and transposition modeling, and the rest of the physical PV simulation can be left to machine learning in hybrid models without a significant increase in the errors. The optimization of the physical model chains is found to be important even in the case of hybrid modeling; therefore, it should become a standard procedure in practical applications. Finally, the hybrid method is only beneficial for at least one year of training data, while in the initial period of the operation of a PV plant, it is advised to stay with optimized physical modeling. The guidelines and recommendations of this paper can help both researchers and practitioners design and optimize their power conversion model to increase the accuracy of the PV power forecasts. • New concept for hybrid physical and machine learning irradiance-to-power conversion. • Comparison of physical, data-driven, and hybrid approaches for two directives. • Physical global tilted irradiance calculation is sufficient in hybrid modeling. • Optimization of physical model chains is important even in hybrid models. • The recommended method depends on the length of the available training data. [ABSTRACT FROM AUTHOR]