Estimation of photovoltaic generated energy and optimum tilt angle from weather data using neural network

DE BERNARDEZ L:S:, BUITRAGO R.H., GARCIA N.O.

SOLAR ENERGY

Año: 2008

0038-092X

Neural networks were used to predict the energy generated by photovoltaic modules from climatic parameters in an Argentinean region. For this purpose, temperature, ambient relative humidity, atmospheric pressure and wind speed data were collected over a year. Also, incident energy on the module plane, generated electric energy and module work temperature were measured. A very good estimation of the energy generated by modules and its maximum work temperature was obtained from information on  the geographical location and climatic parameters. According to our findings, even though direct and diffuse solar radiation data are unknown, neural network may be used not only for an a priori evaluation of solar resource availability and electric energy generation, but also to define the optimum tilt  angle of the photovoltaic installation.