Analysis of the usability of the markov model holding at the estimation of solar radiation of Bilecik region

Before the system is installed region where solar energy will be obtained the potential of that region should be well researched, and installation costs should be well calculated and evaluated. Estimates of solar radiation intensity should be made for the area to be installed. There are many modeling and estimation methods that can be used when a zone is subjected to solar energy potential analysis. Hidden Markov Model (SMM) used in this study is a method that learns and estimates by considering statistical data. Besides, in recent years new models related to Hidden Markov models have been uncovered. Hidden Markov Methods have a increasingly attractive for problems in language, speech, signal processing. Hidden Markov Model it has been used for many applications such as in artificial intelligence, pattern recognition, speech recognition, and modeling of biological sequences. Furthermore also in the data modeling of renewable energy this method has begun to be used, solar energy is also an important renewable energy source, for this reason in this study, it was tried to determine the potential of solar energy, potential by predicting the solar radiation intensity in a certain region in the temperature datas using in hidden markov models. In this study, it is emphasized that it is very easy to determine the solar radiation intensity by using SMM with the help of easily obtainable temperature data. The usability of this method is demonstrated with the application of Bilecik region. In this study, hourly solar temperature data belonging to the region year 2017 and solar radiation intensities used as test data belonging to the region year 2017 were obtained from the Turkish Stote Meteorolojical Service.