A Bayesian approach for solar resource potential assessment using satellite images

The need for a more sustainable and more protective development opens new possibilities for renewable energy. Among the different renewable energy sources, the direct conversion of sunlight into electricity by solar photovoltaic (PV) technology seems to be the most promising and represents a technically viable solution to energy demands. But implantation and deployment of PV energy need solar resource data for utility planning, accommodating grid capacity, and formulating future adaptive policies. Currently, the best approach to determine the solar resource at a given site is based on the use of satellite images. However, the computation of solar resource (non-linear process) from satellite images is unfortunately not straightforward. From a signal processing point of view, it falls within non-stationary, non-linear/non-Gaussian dynamical inverse problems. In this paper, we propose a Bayesian approach combining satellite images and in situ data. We propose original observation and transition functions taking advantages of the characteristics of both the involved type of data. A simulation study of solar irradiance is carried along with this method and a French Guiana solar resource potential map for year 2010 is given