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Accurate prediction of daily solar insolation has been one of the most important issues of solar engineering. The amount of solar insolation on a given location is a vital data for photovoltaic plants. Systems efficiency is easily affected by the changes in solar radiation so, this study is aimed to develop a Least Squares Support Vector Machine (LS-SVM) based intelligent model to predict the next day's solar insolation for taking measures. Daily temperature and insolation data measured by Turkish State Meteorological Service for three years (2000-2002) were used as training data and the values of 2003 used as testing data. Numbers of the days from 1st January, daily mean temperature, daily maximum temperature, sunshine duration and the solar insolation of the day before parameters have been used as inputs to predict the daily solar insolation. The simulations were carried out with SVM Toolbox of MATLAB software. As a conclusion the results show that LS-SVM is a good method in estimating the amount of solar insolation of a given location with 99.294% accuracy.

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This study focuses on the effect of climatic and geographical factors on the performance of photovoltaic systems. For this purpose, 4 different cities (Antalya,İstanbul, Elazıg, and Erzurum) from different climatic zones according to TS 825 Thermal Insulation Requirements in Buildings were selected. The monthly average global and diffuse solar radiation of the cities was calculated numerically with the long-term sunshine duration data (between 1990 and 2012) taken from the Turkish State Meteorological Service. The yearly electric energy generated by the gridconnected photovoltaic systems assumed to be located on the flat roofs of building samples were calculated for each of the cities. PVsyst 6.2.6. software was employed for the calculation of yearly energy yield. Consequently, the maximum photovoltaic output was achieved in Elazıg and the minimum inİstanbul. The optimum tilt angles for Antalya,İstanbul, Elazıg, and Erzurum were obtained as 32 • , 36 • , 32 • , and 35 • , respectively.