Effective Estimation of Hourly Global Solar Radiation Using Machine Learning Algorithms

Abdurrahman Burak Guher,Bulent Yaniktepe,Sakir Tasdemir,Gianluca Coccia

doi:10.1155/2020/8843620

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), K -Nearest Neighbors ( K -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, R 2 , 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.

Highlights

Energy, which is an effective parameter in the development of countries, is increasing rapidly with industry, technological advances, and increasing population
Places that had the same latitude coordinates were selected on the target area, and Machine Learning (ML) algorithms were employed for high-accuracy Global Solar Radiation (GSR) estimation
A comparative evaluation was made by developing models based on four different ML (MFFNN, KNN, SVR-based Library for Support Vector Machines (LibSVM), and M5 rules) algorithms to predict the Hourly Average Global Solar Radiation (HAGSR) of the provinces of Kahramanmaras and Isparta, which are located on the same latitude coordinates of the Mediterranean Region

Summary

Introduction

Energy, which is an effective parameter in the development of countries, is increasing rapidly with industry, technological advances, and increasing population. As a result of this, investments in solar energy for electricity generation are increasing rapidly in recent years with technological advances in solar energy, global climate change, dependence on other countries, and other environmental factors. In this context, photovoltaic (PV), as one of the usages of solar energy application areas, is intensively applied in order to produce electricity [2, 3]

Methods

Results

Conclusion