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Comparison of an Engineering Model and a Statistical Model to Estimate the Performance of a Solar Photovoltaic System

[+] Author Affiliations
Nelson Fumo

The University of Texas at Tyler, Tyler, TX

Juan Carlo Zambrano

Universidad Nacional Experimental del Táchira, San Cristóbal, Táchira, Venezuela

Vicente Bortone

Johnson Controls Inc., Lenexa, KS

Paper No. ES2015-49044, pp. V002T11A001; 5 pages
  • ASME 2015 9th International Conference on Energy Sustainability collocated with the ASME 2015 Power Conference, the ASME 2015 13th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2015 Nuclear Forum
  • Volume 2: Photovoltaics; Renewable-Non-Renewable Hybrid Power System; Smart Grid, Micro-Grid Concepts; Energy Storage; Solar Chemistry; Solar Heating and Cooling; Sustainable Cities and Communities, Transportation; Symposium on Integrated/Sustainable Building Equipment and Systems; Thermofluid Analysis of Energy Systems Including Exergy and Thermoeconomics; Wind Energy Systems and Technologies
  • San Diego, California, USA, June 28–July 2, 2015
  • Conference Sponsors: Advanced Energy Systems Division, Solar Energy Division
  • ISBN: 978-0-7918-5685-7
  • Copyright © 2015 by ASME


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.

Copyright © 2015 by ASME



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