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Many empirical models for predicting the photovoltaic output power are available in the literature. However, selecting the most suitable for a specific climate is a difficult task as no comparative studies have been addressed. This work aims to evaluate eighteen empirical models existing in the literature until today and which only use meteorological data as input. The models selected according to their simplicity and accuracy were validated and their accuracy has been evaluated versus the experimental measurements recovered from the photovoltaic installation of the Faculty of Science and Technology of Tangier. Coefficient of determination ( R2 ), mean absolute error, mean absolute percentage error, root mean square error, and its normalised version have been used as evaluation indexes of the prediction model. Results show a good correlation between the measured values and those predicted by the models, where recorded values of  R2  were between 99.8% and 96.4% as the maximum and minimum values. Furthermore, this study gave good normalised root mean square error scores between 7.115% and 27.347% as the most favourable and unfavourable errors. This indicator generally provides better information on the prediction model quality. The selection of the most suitable model to use was also carried out taking into account climatic changes such as sunny, cloudy and rainy days. Therefore, the models are tested based on three days of different climates for high-resolution photovoltaic simulation output. Abbreviations  ANN: artificial neural network; FST: Faculty of Science and Technology; FSTT: Faculty of Science and Technology of Tangier; PV: photovoltaic; STC: standard test conditions.

Performance evaluation and experimental validation of different empirical models for predicting photovoltaic output power