The solar energy has potential future effectiveness in a variety of areas and it helps to decrease the dominance of fossil fuels, which cause atmospheric pollution, global warming and climate change impacts. In the literature, there are different methodologies for its modeling, but this study suggests the harmonic analysis application to solar irradiation and sunshine duration data for more refined relevant prediction of solar irradiation. The basis of the methodology is combined application of the HARmonic and the classical LINear regression analyses, and therefore, it is referred to as the HarLin model. It isolates first the periodicity from the daily averages of records and then the linear regression analysis is applied elegantly to first order stationary data. The results are tested and compared with the Adaptive-Neuro Fuzzy Inference System (ANFIS) model based on the Sugeno fuzzy logic inference system and Angstrom-Prescott model in the form of a linear regression analysis. In the application, three solar irradiation sites are considered from different solar energy potential locations in Turkey, namely, at Adana, Gaziantep and Silifke cities. The predictions by the HarLin model appear more successful than ANFIS and the classical Angstrom-Prescott approaches. (C) 2015 Elsevier Ltd. All rights reserved.