At the design stage of a solar photovoltaic (PV) system, equipment’s information from the specifications provided by manufacturers is the most reliable information. Parameters used to describe the performance are obtained under laboratory conditions, but the information is the appropriate for estimating the performance of the components of the solar PV system. When a system is in operation, the engineering models used at the design stage can also be used to predict the performance of the system. However, under real conditions, many factors can affect the performance which suggests that statistical models developed with field data could give better results to predict the performance of a solar PV system. Experimental data used in this study correspond to the energy generated by a 7.5 kW PV system installed to supply electricity to a research house at the University of Texas at Tyler, as well as the outdoor temperature and global horizontal solar radiation (as energy) recorder on site. The data is used to develop a multiple linear regression model and compare this model with an engineering model. Results show that the statistical model has a better quality than the engineering model.

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