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
