First order Markov chain model for generating synthetic "typical days" series of global irradiation in order to design photovoltaic stand alone systems

In order to elaborate a generator of "typical days" sequences necessary for sizing photovoltaic (PV) systems, this paper develops a methodology to cla ss daily global solar irradiation based on a first order Markov chain model . The method has been applied for two stations in Corsica (France), and we have limited the number of discriminant parameters computed from the hourly clearness index kt(h), The classification is based on Ward's method, check ed by discriminant analyses. Previous works have shown that the different d ays could be clustered in three groups with distinct mean values and lower standard deviations. After checking the dependence and the stationary of th e Markov's chain. the presented model allows one to compute the simulated m arginal probabilities pi with a good correlation with the experimental ones (MBE is an element of [-0.154;0.151]: mean bias error, MBE). A good correl ation has been observed between the simulated and experimental transition p robability matrices for both meteorological sites (Vignola MBE = 0, RMSE is an element of [0,035;0.056]: RMBE(%,) is an element of [0,17;3.59]; Campo del'Oro MBE = 0, RMSE is an element of [0.016:0.105], RMBE(%) is an element of [-2, 15,4.26]), At least, the sizing of PV systems has shown that the k W h costs, computed from real and simulated systems, present weak relative errors belonging to the range -13.3% to +4.2'%,, checking the global effici ency of the methodology. (C) 3000 Elsevier Science Ltd. All rights reserved .