A Soft Computing Model for Optimizing Significant Parameters of Insolation Distribution in BIPV Application

Despite huge development in prediction of solar radiation data, there is a clear disconnect in extraction and effective utilization of pertinent information from such data. Use of quality function deployment (QFD) can smartly identify the most significant statistics representing insolation availability for a solar PV installation. A MATLAB program has been used to build the annual frequency distribution of hourly insolation over any module plane at a given site location. Descriptic statistical analysis of such distributions is done through MINITAB. To make the analysis more meaningful, composite frequency distribution for a building integrated photo voltaic (BIPV) set up has been considered, which is formed by weighted summation of insolation distributions for different module planes used in the installation. The most influential statistics of the composite distribution have been optimized through artificial neural network computation. This novel approach is expected to be a very powerful tool for the BIPV system designers.