MODELLING OF DIFFERENT MOTHER WAVELET TRANSFORMS WITH ARTIFICIAL NEURAL NETWORKS FOR ESTIMATION OF SOLAR RADIATION

Abstract

IIn recent years, the interest in renewable energy sources has increased due to environmental damage and, the increasing costs of fossil fuel resources, whose current reserves have decreased. Solar energy, an environmentally friendly, clean and sustainable energy source, is one of the most important renewable energy sources. The amount of electrical energy produced from solar energy largely depends on the intensity of solar radiation. For this reason, it is essential to know and accurately predict the characteristics of the solar radiation intensity of the relevant region for the healthy sustainability of the existing solar energy systems and the systems planned to be installed. For this purpose, a two-stage forecasting model was developed using the hourly solar radiation intensity of 2014 in a region in Turkey. In the first stage of the study, the second month of each season was selected to investigate the seasonal effects of the region and large, medium, and small-scale events in the study area were examined using discrete wavelet transform. The performances of different mother wavelets in the Artificial Neural Network model with Wavelet Transform (W-ANN) are compared in the second stage. July, the most successful estimation result in seasonal solar radiation intensity was obtained. The most successful RMSE values for January, April, July and October were 65,9471W/m^2, 74,3183 W/m^2, 54,3868 W/m^2, 78,4085 W/m^2 respectively, the coiflet mother wavelet measured it.