Izvorni znanstveni članak

https://doi.org/10.17559/TV-20200425151543

Determining of Solar Power by Using Machine Learning Methods in a Specified Region

A. Burak Guher ; OsmaniyeKorkut Ata University, Osmaniye Vocational School, Karacaoglan Campus, 80000, Osmaniye, Turkey
Sakir Tasdemir ; Selcuk University, Technology Faculty, Computer Engineering, Alaaddin Keykubat Campus, Selcuklu, 42075, Konya, Turkey
Bulent Yaniktepe* ; Osmaniye Korkut Ata University, Engineering Faculty, Energy Systems Eng. Dept., Karacaoglan Campus, 80000, Osmaniye, Turkey

Sažetak

In this study, it is aimed to estimate the solar power according to the hourly meteorological data of the specified location measured between 2002 and 2006 by using different Machine Learning (ML) algorithms. Data Mining Processes (DMP) were used to select the most appropriate input variables from these measured data. Data groups created using DMP were evaluated according to three different ML algorithms such as Artificial Neural Network (ANN), Support Vector Regression (SVR) and K-Nearest Neighbors (KNN). It can be concluded that DMP-ML based prediction models are more successful than models developed using all available data. The most successful model developed among these models estimated the hourly solar power potential with an accuracy of 97%. Also, different error measurement statistics were used to evaluate ML algorithms. According to Symmetric Mean Absolute Percentage Error, 6.12%, 7.22% and 12.72% values were found in the most successful prediction models developed using ANN, KNN and SVR, respectively. In addition, from the meteorological data used in this study the most effective data on solar power as a result of DMP were shown to be Temperature and Hourly Sunshine Duration.

Ključne riječi

data mining processes; machine learning; optimal data analysis; solar power

Hrčak ID:

261301

URI

https://hrcak.srce.hr/261301

Datum izdavanja:

15.8.2021.

Posjeta: 951 *