Open AccessMODELING OF SOLAR RADIATION WITH A NEURAL NETWORK
Author(s) -
Valentin Stoyanov,
Ivaylo Stoyanov,
Teodor Iliev
Publication year - 2018
Publication title -
journal of engineering studies and research
Language(s) - English
Resource type - Journals
eISSN - 2344-4932
pISSN - 2068-7559
DOI - 10.29081/jesr.v24i3.55
Subject(s) - artificial neural network , azimuth , radiation , latitude , data set , orientation (vector space) , surface (topology) , set (abstract data type) , computer science , remote sensing , meteorology , artificial intelligence , geodesy , mathematics , optics , physics , geometry , geology , programming language
Modeling of solar radiation with neural network could be used for real-time calculations of the radiation on tilted surfaces with different orientations. In the artificial neural network (ANN), latitude, day of the year, slope, surface azimuth and average daily radiation on horizontal surface are inputs, and average daily radiation on tilted surface of definite orientation is output. The possible ANN structure, the size of training data set, the number of hidden neurons, and the type of training algorithms were analyzed in order to identify the most appropriate model. The same ANN structure was trained and tested using data generated from the Klein and Theilacker model and long-term measurements. Reasonable accuracy was obtained for all predictions for practical need.