Scopus
🔓 Açık Erişim YÖKSİS Eşleşti
Determining of solar power by using machine learning methods in a specified region
Tehnicki Vjesnik · Ağustos 2021
YÖKSİS Kayıtları
Determining of Solar Power by Using Machine Learning Methods in a Specified Region
Tehnicki vjesnik - Technical Gazette · 2021 scopus
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Makale Bilgileri
DergiTehnicki Vjesnik
Yayın TarihiAğustos 2021
Cilt / Sayfa28 · 1471-1479
Scopus ID2-s2.0-85113717096
Erişim🔓 Açık Erişim
Özet
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.
Yazarlar (3)
1
A. Burak Guher
2
Şakir Taşdemir
3
Bulent Yaniktepe
Anahtar Kelimeler
Data mining processes
Machine learning
Optimal data analysis
Solar power
Kurumlar
Osmaniye Korkut Ata University
Osmaniye Turkey
Selçuk Üniversitesi
Selçuklu Turkey
University of Osmaniye Korkut Ata
Osmaniye Turkey
Metrikler
7
Atıf
3
Yazar
4
Anahtar Kelime