Forecast Analysis of Renewable Solar Energy Production Using Meteorological Data with Machine Learning Methods

Makine Öğrenmesi Yöntemleriyle Yenilenebilir Güneş Enerjisi Üretiminin Meteorolojik Veriler Kullanılarak Tahmin Analizi

Authors

  • Naciye Macit Sezikli Istanbul Gelisim University, Vocational School
  • Ümit Alkan İstanbul Gelisim University, Faculty of Engineering and Architecture, Department of Computer Engineering
  • Metin Zontul Sivas University of Science and Technology, Faculty of Engineering and Natural Sciences, Department of Computer Engineering
  • Zeynep Elabiad International Road Federation, USA

Keywords:

Solar Energy, Solar Energy Plant, Meteorological Parameters, Machine Learning, Machine Learning Methods

Abstract

Solar energy power plants play a significant role in meeting the demand for sustainable and clean energy. However, variable weather conditions, seasonal effects, and similar factors can result in the need for energy overproduction to be stored or lead to costs associated with energy deficiency. These situations can result in inefficiencies in solar energy production. The objective of this study is to predict energy production, increase efficiency, and develop more sustainable energy strategies by using machine learning methods with data obtained from meteorological and solar energy panels. This study aims to assess the results achieved by existing models and compare their successes. The Random Forest algorithm, which achieved the highest R2 score, also obtained significantly lower values for MSE, RMSE, and MAE. This indicates that the Random Forest algorithm performs the best among the algorithms used in this study. This ranking of success is followed by Decision Trees and K-Nearest neighbors.

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Author Biographies

Naciye Macit Sezikli, Istanbul Gelisim University, Vocational School

Naciye Macit Sezikli received her B.Sc. degree in Computer Systems from the Faculty of Technical Education, Suleyman Demirel University, Turkiye, in 2009. She received her M.Sc. degree in Mechatronics Engineering from the Faculty of Engineering and Architecture, Istanbul Gelisim University, Turkiye, in 2023. She is currently working at Istanbul Gelisim University as a Lecturer.

Ümit Alkan, İstanbul Gelisim University, Faculty of Engineering and Architecture, Department of Computer Engineering

Ümit Alkan received his B.Sc. degree in Physics from the Faculty of Arts and Sciences, Department, Yıldız Technical University, Turkiye, in 2001. He received his M.Sc. degree in Physics from the Institute of Science, Yıldız Technical University, Turkiye, in 2004. He received his Ph.D. degree in Physics from the Institute of Science, Yıldız Technical University, Turkiye, in 2011. He is currently working at İstanbul Gelişim University as a Doctor Lecturer of the Faculty of Engineering Archıtecture.

Metin Zontul, Sivas University of Science and Technology, Faculty of Engineering and Natural Sciences, Department of Computer Engineering

Metin Zontul received his B.Sc. degree in Computer Engineering from the Faculty of Engineering, Middle East Technical University, Turkiye, in 1993. Later, he received his M.Sc. degree in Computer-Aided Design, Production, and Programming from Erciyes University, Turkiye, in 1996. Finally, he received his Ph.D. degree in Numerical Methods from Cumhuriyet University, Turkiye, in 2004. He is currently working at Sivas University of Science and Technology as Dean of the Faculty of Engineering and Natural Sciences.

Zeynep Elabiad, International Road Federation, USA

Zeynep Elabiad received her B.Sc. degree in Computer Systems from the Faculty of Technical Education at Suleyman Demirel University, Turkey, in 2009. She earned her M.Sc. degree in Computer Engineering from the Faculty of Engineering at Istanbul Aydin University, Turkiye, in 2013. She obtained her second M.Sc. degree in Data Science from Indiana University Bloomington, USA, in 2023. She is currently working as an IT manager at the International Road Federation in the US.

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Published

31-07-2024

How to Cite

[1]
N. Macit Sezikli, Ümit Alkan, M. Zontul, and Z. Elabiad, “Forecast Analysis of Renewable Solar Energy Production Using Meteorological Data with Machine Learning Methods: Makine Öğrenmesi Yöntemleriyle Yenilenebilir Güneş Enerjisi Üretiminin Meteorolojik Veriler Kullanılarak Tahmin Analizi”, JAST, vol. 17, no. 2, pp. 180–202, Jul. 2024.

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Vol. 17 No. 2 (2024)

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