Hybrid Deep Learning Framework for Short-Term Electricity Generation Forecasting in Türkiye Using Multi-Source Data

Hamdullah Karamollaoğlu

doi:10.5152/tepes.2025.25029

Hybrid Deep Learning Framework for Short-Term Electricity Generation Forecasting in Türkiye Using Multi-Source Data

Abstract

The growing complexity of electricity generation, driven by the diversification of energy sources and the integration of renewables, makes accurate short-term forecasting crucial for grid stability and energy security. This study proposes a deep learning-based hybrid forecasting model designed for Türkiye’s dynamic energy landscape. Using hourly electricity production data from December 1, 2019, to March 1, 2025, sourced from the EPİAŞ Transparency Platform, the model analyzes generation patterns across 17 different sources, including both fossil fuels and renewables. The proposed architecture combines Long Short- Term Memory networks and Transformer models to effectively capture complex time-dependent relationships in electricity generation. To improve accuracy, preprocessing techniques such as time-based interpolation, normalization, and principal component analysis were applied. Experimental results demonstrate strong forecasting performance, achieving a mean absolute error of 589.50, a root mean squared error of 762.41, and a coefficient of determination (R2) of 0.98017 for 1-hour ahead predictions, and an R2 of 0.87813 for 1-day ahead predictions. These findings underline the model’s potential to support operational planning, market regulation, and policy-making processes, particularly in emerging economies with dynamic and heterogeneous energy infrastructures.

Keywords

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Details

Primary Language

English

Subjects

Electrical Energy Generation (Incl. Renewables, Excl. Photovoltaics)

Journal Section

Research Article

Authors

Hamdullah Karamollaoğlu ^*
0000-0001-6419-2249
Türkiye

Publication Date

October 30, 2025

Submission Date

July 16, 2025

Acceptance Date

September 9, 2025

Published in Issue

Year 2025 Volume: 5 Number: 3

DOI

https://doi.org/10.5152/tepes.2025.25029

IZ

https://izlik.org/JA59UW92YZ

Cite

RIS / Bibtex

APA

Karamollaoğlu, H. (2025). Hybrid Deep Learning Framework for Short-Term Electricity Generation Forecasting in Türkiye Using Multi-Source Data. Turkish Journal of Electrical Power and Energy Systems, 5(3), 152-160. https://doi.org/10.5152/tepes.2025.25029

AMA

1.Karamollaoğlu H. Hybrid Deep Learning Framework for Short-Term Electricity Generation Forecasting in Türkiye Using Multi-Source Data. TEPES. 2025;5(3):152-160. doi:10.5152/tepes.2025.25029

Chicago

Karamollaoğlu, Hamdullah. 2025. “Hybrid Deep Learning Framework for Short-Term Electricity Generation Forecasting in Türkiye Using Multi-Source Data”. Turkish Journal of Electrical Power and Energy Systems 5 (3): 152-60. https://doi.org/10.5152/tepes.2025.25029.

EndNote

Karamollaoğlu H (October 1, 2025) Hybrid Deep Learning Framework for Short-Term Electricity Generation Forecasting in Türkiye Using Multi-Source Data. Turkish Journal of Electrical Power and Energy Systems 5 3 152–160.

IEEE

[1]H. Karamollaoğlu, “Hybrid Deep Learning Framework for Short-Term Electricity Generation Forecasting in Türkiye Using Multi-Source Data”, TEPES, vol. 5, no. 3, pp. 152–160, Oct. 2025, doi: 10.5152/tepes.2025.25029.

ISNAD

Karamollaoğlu, Hamdullah. “Hybrid Deep Learning Framework for Short-Term Electricity Generation Forecasting in Türkiye Using Multi-Source Data”. Turkish Journal of Electrical Power and Energy Systems 5/3 (October 1, 2025): 152-160. https://doi.org/10.5152/tepes.2025.25029.

JAMA

1.Karamollaoğlu H. Hybrid Deep Learning Framework for Short-Term Electricity Generation Forecasting in Türkiye Using Multi-Source Data. TEPES. 2025;5:152–160.

MLA

Karamollaoğlu, Hamdullah. “Hybrid Deep Learning Framework for Short-Term Electricity Generation Forecasting in Türkiye Using Multi-Source Data”. Turkish Journal of Electrical Power and Energy Systems, vol. 5, no. 3, Oct. 2025, pp. 152-60, doi:10.5152/tepes.2025.25029.

Vancouver

1.Hamdullah Karamollaoğlu. Hybrid Deep Learning Framework for Short-Term Electricity Generation Forecasting in Türkiye Using Multi-Source Data. TEPES. 2025 Oct. 1;5(3):152-60. doi:10.5152/tepes.2025.25029