Time Series Analysis of Solar Energy Production Based on Weather Conditions

Mehmet Bülüç; Onur Sevli; Lokman Yünlü

doi:10.54287/gujsa.1797659

Time Series Analysis of Solar Energy Production Based on Weather Conditions

Abstract

This study investigates the impact of weather conditions on solar energy production by comparing the performance of different time series forecasting models. Production data obtained from the İkitelli Solar Power Plant in Istanbul, together with simultaneous meteorological variables such as sunshine duration, cloudiness, humidity, and temperature, were analyzed. Three forecasting models—ARIMA, LSTM, and FB-Prophet—were implemented to evaluate their predictive performance. The accuracy of the models was assessed using widely accepted statistical metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The results show that the ARIMA model achieved the highest accuracy in short-term forecasting with the lowest error rates, demonstrating its effectiveness in handling stationary time series. The LSTM model, a deep learning approach, proved successful in capturing long-term dependencies, offering a robust alternative despite requiring larger datasets for optimal performance. The FB-Prophet model stood out for its ability to account for seasonality and trends but exhibited lower accuracy in short-term fluctuations compared to ARIMA and LSTM. Additionally, the analysis revealed that solar energy production is strongly correlated with weather conditions. In particular, an increase in sunshine duration positively influenced electricity generation, while greater cloud cover led to significant reductions in production levels. These findings highlight the importance of incorporating meteorological data into forecasting models to enhance the accuracy and reliability of renewable energy predictions. Furthermore, the study emphasizes that selecting the appropriate forecasting model according to data characteristics is critical for effective energy management. The outcomes provide methodological insights that may contribute to the optimization of solar energy projects and the integration of renewable energy into sustainable energy strategies.

Keywords

Supporting Institution

This research received no specific grant from any funding agency.

Ethical Statement

No ethical approval was required for this study.

Thanks

The authors express their gratitude to the Istanbul Metropolitan Municipality and World Weather Online for providing the data.

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Details

Primary Language

English

Subjects

Deep Learning, Neural Networks, Photovoltaic Power Systems, Solar Energy Systems

Journal Section

Research Article

Authors

Mehmet Bülüç ^*
0009-0003-8089-463X
Türkiye

Onur Sevli
0000-0002-8933-8395
Türkiye

Lokman Yünlü
0000-0003-1625-995X
Türkiye

Publication Date

December 31, 2025

Submission Date

October 7, 2025

Acceptance Date

December 12, 2025

Published in Issue

Year 2025 Volume: 12 Number: 4

DOI

https://doi.org/10.54287/gujsa.1797659

IZ

https://izlik.org/JA55HJ76NE

Cite

RIS / Bibtex

APA

Bülüç, M., Sevli, O., & Yünlü, L. (2025). Time Series Analysis of Solar Energy Production Based on Weather Conditions. Gazi University Journal of Science Part A: Engineering and Innovation, 12(4), 1060-1077. https://doi.org/10.54287/gujsa.1797659

AMA

1.Bülüç M, Sevli O, Yünlü L. Time Series Analysis of Solar Energy Production Based on Weather Conditions. GU J Sci, Part A. 2025;12(4):1060-1077. doi:10.54287/gujsa.1797659

Chicago

Bülüç, Mehmet, Onur Sevli, and Lokman Yünlü. 2025. “Time Series Analysis of Solar Energy Production Based on Weather Conditions”. Gazi University Journal of Science Part A: Engineering and Innovation 12 (4): 1060-77. https://doi.org/10.54287/gujsa.1797659.

EndNote

Bülüç M, Sevli O, Yünlü L (December 1, 2025) Time Series Analysis of Solar Energy Production Based on Weather Conditions. Gazi University Journal of Science Part A: Engineering and Innovation 12 4 1060–1077.

IEEE

[1]M. Bülüç, O. Sevli, and L. Yünlü, “Time Series Analysis of Solar Energy Production Based on Weather Conditions”, GU J Sci, Part A, vol. 12, no. 4, pp. 1060–1077, Dec. 2025, doi: 10.54287/gujsa.1797659.

ISNAD

Bülüç, Mehmet - Sevli, Onur - Yünlü, Lokman. “Time Series Analysis of Solar Energy Production Based on Weather Conditions”. Gazi University Journal of Science Part A: Engineering and Innovation 12/4 (December 1, 2025): 1060-1077. https://doi.org/10.54287/gujsa.1797659.

JAMA

1.Bülüç M, Sevli O, Yünlü L. Time Series Analysis of Solar Energy Production Based on Weather Conditions. GU J Sci, Part A. 2025;12:1060–1077.

MLA

Bülüç, Mehmet, et al. “Time Series Analysis of Solar Energy Production Based on Weather Conditions”. Gazi University Journal of Science Part A: Engineering and Innovation, vol. 12, no. 4, Dec. 2025, pp. 1060-77, doi:10.54287/gujsa.1797659.

Vancouver

1.Mehmet Bülüç, Onur Sevli, Lokman Yünlü. Time Series Analysis of Solar Energy Production Based on Weather Conditions. GU J Sci, Part A. 2025 Dec. 1;12(4):1060-77. doi:10.54287/gujsa.1797659