Improving Accuracy in Solar Power Plant Power Generation Prediction: A Hybrid Model Proposal

Necati Aksoy; İstemihan Genç

doi:10.5152/tepes.2024.24027

Improving Accuracy in Solar Power Plant Power Generation Prediction: A Hybrid Model Proposal

Abstract

Renewable energy sources play an increasingly important role in meeting global energy demand while reducing carbon emissions and overall energy costs. Accurate forecasting of power generation in solar power plants is crucial for effective energy management and operational planning. This study proposes a novel hybrid prediction model that integrates several widely used machine learning algorithms to enhance the accuracy of solar power generation forecasting. Based on real meteorological and production data, the proposed hybrid model significantly outperforms individual prediction models. By incorporating meteorological parameters, the model provides more reliable short-term and long-term power predictions, thereby supporting improved decision-making processes in solar power plant operations. The results highlight the advantages of the proposed approach and offer valuable insights into improving the predictability and operational efficiency of solar power plants.

Keywords

References

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Details

Primary Language

English

Subjects

Photovoltaic Power Systems

Journal Section

Research Article

Authors

Necati Aksoy ^* This is me
0000-0003-1496-2916
Türkiye

İstemihan Genç
0000-0001-7077-8895
Türkiye

Publication Date

February 28, 2025

Submission Date

September 5, 2024

Acceptance Date

October 11, 2024

Published in Issue

Year 2025 Volume: 5 Number: 1

DOI

https://doi.org/10.5152/tepes.2024.24027

IZ

https://izlik.org/JA27HS36UN

Cite

RIS / Bibtex

APA

Aksoy, N., & Genç, İ. (2025). Improving Accuracy in Solar Power Plant Power Generation Prediction: A Hybrid Model Proposal. Turkish Journal of Electrical Power and Energy Systems, 5(1), 10-18. https://doi.org/10.5152/tepes.2024.24027

AMA

1.Aksoy N, Genç İ. Improving Accuracy in Solar Power Plant Power Generation Prediction: A Hybrid Model Proposal. TEPES. 2025;5(1):10-18. doi:10.5152/tepes.2024.24027

Chicago

Aksoy, Necati, and İstemihan Genç. 2025. “Improving Accuracy in Solar Power Plant Power Generation Prediction: A Hybrid Model Proposal”. Turkish Journal of Electrical Power and Energy Systems 5 (1): 10-18. https://doi.org/10.5152/tepes.2024.24027.

EndNote

Aksoy N, Genç İ (February 1, 2025) Improving Accuracy in Solar Power Plant Power Generation Prediction: A Hybrid Model Proposal. Turkish Journal of Electrical Power and Energy Systems 5 1 10–18.

IEEE

[1]N. Aksoy and İ. Genç, “Improving Accuracy in Solar Power Plant Power Generation Prediction: A Hybrid Model Proposal”, TEPES, vol. 5, no. 1, pp. 10–18, Feb. 2025, doi: 10.5152/tepes.2024.24027.

ISNAD

Aksoy, Necati - Genç, İstemihan. “Improving Accuracy in Solar Power Plant Power Generation Prediction: A Hybrid Model Proposal”. Turkish Journal of Electrical Power and Energy Systems 5/1 (February 1, 2025): 10-18. https://doi.org/10.5152/tepes.2024.24027.

JAMA

1.Aksoy N, Genç İ. Improving Accuracy in Solar Power Plant Power Generation Prediction: A Hybrid Model Proposal. TEPES. 2025;5:10–18.

MLA

Aksoy, Necati, and İstemihan Genç. “Improving Accuracy in Solar Power Plant Power Generation Prediction: A Hybrid Model Proposal”. Turkish Journal of Electrical Power and Energy Systems, vol. 5, no. 1, Feb. 2025, pp. 10-18, doi:10.5152/tepes.2024.24027.

Vancouver

1.Necati Aksoy, İstemihan Genç. Improving Accuracy in Solar Power Plant Power Generation Prediction: A Hybrid Model Proposal. TEPES. 2025 Feb. 1;5(1):10-8. doi:10.5152/tepes.2024.24027