A statistical technique to characterize insolation data for use in photovoltaic (PV) systems is presented. We start by examining the frequency distribution of long-term insolation data. The histogram is generated for observed insolation for a particular hour over a month for a number of years. It is fitted to three distributions (Weibull, beta and log normal). Four goodness-of-fit criteria are employed in checking the best fit. These are Chi-square, Kolmogorov-Smironov, Cramer-Von Mises-Smironov, and log-likelihood. SOLMET data from Sterling, Va, Raleigh-Durham, N. C. and Miami, Fla are analyzed. It is found that the beta distribution fits the long-term hourly global horizontal insolation data best for threse three southeastern U. S. locations.
