Univariate deep learning models for short-term electricity load forecasting from renewables

Fatma Başoğlu Kabran; Kamil Demirberk Ünlü

doi:10.31801/cfsuasmas.1643466

Univariate deep learning models for short-term electricity load forecasting from renewables

Abstract

Renewable energy offers a cost-effective, carbon-free solution for energy needs, while protecting the environment. Accurate forecasting of electricity generation from renewable sources is crucial for the efficiency of modern power grids. This study employs a univariate deep learning approach to predict daily renewable energy generation, evaluating Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) as candidate models. Five performance metrics—mean absolute error, root mean squared error, mean absolute percentage error, mean absolute scaled error and the coefficient of determination—are employed to assess the forecasting power of the algorithms. The empirical results show that CNN outperforms other models, achieving an $R^2$ of almost $94\%$. This research shows that the univariate model based on historical data of electricity load generated from renewables can accurately predict day-ahead electricity load, even without meteorological data.

Keywords

References

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Details

Primary Language

English

Subjects

Statistical Analysis, Applied Statistics

Journal Section

Research Article

Authors

Fatma Başoğlu Kabran ^*
0000-0002-0212-5785
Türkiye

Kamil Demirberk Ünlü
0000-0002-2393-6691
Türkiye

Publication Date

December 24, 2025

Submission Date

February 20, 2025

Acceptance Date

June 4, 2025

Published in Issue

Year 2025 Volume: 74 Number: 4

DOI

https://doi.org/10.31801/cfsuasmas.1643466

IZ

https://izlik.org/JA49NF68HM

Cite

RIS / Bibtex

APA

Başoğlu Kabran, F., & Ünlü, K. D. (2025). Univariate deep learning models for short-term electricity load forecasting from renewables. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, 74(4), 670-686. https://doi.org/10.31801/cfsuasmas.1643466

AMA

1.Başoğlu Kabran F, Ünlü KD. Univariate deep learning models for short-term electricity load forecasting from renewables. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2025;74(4):670-686. doi:10.31801/cfsuasmas.1643466

Chicago

Başoğlu Kabran, Fatma, and Kamil Demirberk Ünlü. 2025. “Univariate Deep Learning Models for Short-Term Electricity Load Forecasting from Renewables”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 74 (4): 670-86. https://doi.org/10.31801/cfsuasmas.1643466.

EndNote

Başoğlu Kabran F, Ünlü KD (December 1, 2025) Univariate deep learning models for short-term electricity load forecasting from renewables. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 74 4 670–686.

IEEE

[1]F. Başoğlu Kabran and K. D. Ünlü, “Univariate deep learning models for short-term electricity load forecasting from renewables”, Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat., vol. 74, no. 4, pp. 670–686, Dec. 2025, doi: 10.31801/cfsuasmas.1643466.

ISNAD

Başoğlu Kabran, Fatma - Ünlü, Kamil Demirberk. “Univariate Deep Learning Models for Short-Term Electricity Load Forecasting from Renewables”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 74/4 (December 1, 2025): 670-686. https://doi.org/10.31801/cfsuasmas.1643466.

JAMA

1.Başoğlu Kabran F, Ünlü KD. Univariate deep learning models for short-term electricity load forecasting from renewables. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2025;74:670–686.

MLA

Başoğlu Kabran, Fatma, and Kamil Demirberk Ünlü. “Univariate Deep Learning Models for Short-Term Electricity Load Forecasting from Renewables”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, vol. 74, no. 4, Dec. 2025, pp. 670-86, doi:10.31801/cfsuasmas.1643466.

Vancouver

1.Fatma Başoğlu Kabran, Kamil Demirberk Ünlü. Univariate deep learning models for short-term electricity load forecasting from renewables. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2025 Dec. 1;74(4):670-86. doi:10.31801/cfsuasmas.1643466

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Verimlilik Dergisi

https://doi.org/10.51551/verimlilik.1894768