Artificial intelligence applied to the thermal characterization of building integrated photovoltaic technologies

Resumen

Building Integrated Photovoltaic (BIPV) systems aim not only to generate part of the electricity consumed by the edifice, but also to reduce the environmental impact, such as the Green House Gases emissions produced by the generation of electricity by fossil fuels and the land area that would be required when installed the PV devices in floor. Nevertheless, the thermal behaviour of the PV modules has an impact into the indoor temperature of the building. To study the thermal effect produced by the environmental factors into the module temperature, Artificial intelligence, i.e. Genetic Programming (GP), is applied to one year of data of crystalline silicone PV modules mounted as building elements at outdoor operating conditions to get the model which best describes the thermal behaviour. Two well-known models guided the algorithm, the ones proposed by Ross and by Sandia Laboratories. From the application of Genetic Programming, a model was obtained which calculates the module temperature with less than 1% error. Six constants parameters are obtained for the thermal behaviour of crystalline silicon PV technology, fitting an equation whose structure reflects the two well-known models of Ross and Sandia.