Effective Estimation of Hourly Global Solar Radiation Using Machine Learning Algorithms

Abstract

The precise estimation of solar radiation is of great importance in solar energy applications with respect to installation and capacity. In estimate modelling on selected target locations, various computer-based and experimental methods and techniques are employed. In the present study, the Multilayer Feed-Forward Neural Network (MFFNN), <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" id="M1"> <a:mi>K</a:mi> </a:math> -Nearest Neighbors ( <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" id="M2"> <c:mi>K</c:mi> </c:math> -NN), a Library for Support Vector Machines (LibSVM), and M5 rules algorithms, which are among the Machine Learning (ML) algorithms, were used to estimate the hourly average solar radiation of two geographic locations on the same latitude. The input variables that had the most impact on solar radiation were identified and grouped as a result of 29 different applications that were developed by using 6 different feature selection methods with Waikato Environment for Knowledge Analysis (WEKA) software. Estimation models were developed by using the selected data groups and all input variables for each target location. The results show that the estimations developed with the feature selection method were more successful for target locations, and the radiation potentials were similar. The performance of the estimation models was evaluated by comparing each model with different statistical indicators and with previous studies. According to the RMSE, MAE, <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" id="M3"> <e:msup> <e:mrow> <e:mi>R</e:mi> </e:mrow> <e:mrow> <e:mn>2</e:mn> </e:mrow> </e:msup> </e:math> , and SMAPE statistical scales, the results of the most successful estimation models that were developed with MFFNN were 0.0508-0.0536, 0.0341-0.0352, 0.9488-0.9656, and 7.77%-7.79%, respectively.

Topics & Concepts