Extensive comparison of physical models for photovoltaic power forecasting

Martin János Mayer and Gyula Gróf

Applied Energy, 2021, vol. 283, issue C, No S0306261920316330

Abstract: Forecasting the power production of grid-connected photovoltaic (PV) power plants is essential for both the profitability and the prospects of the technology. Physically inspired modelling represents a common approach in calculating the expected power output from numerical weather prediction data. The model selection has a high effect on physical PV power forecasting accuracy, as the difference between the most and least accurate model chains is 13% in mean absolute error (MAE), 12% in root mean square error (RMSE), and 23–33% in skill scores for a PV plant on average. The power forecast performance analysis performed and verified for one-year 15-min resolution production data of 16 PV plants in Hungary for day-ahead and intraday time horizons on all possible combinations of nine direct and diffuse irradiance separation, ten tilted irradiance transposition, three reflection loss, five cell temperature, four PV module performance, two shading loss, and three inverter models.

Keywords: Photovoltaic forecast; Power prediction; Grid-connected photovoltaic plants; Physical approach; PV simulation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (74)

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DOI: 10.1016/j.apenergy.2020.116239

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