Generative adversarial network for load data generation: Türkiye energy market case

Bilgi Yılmaz

doi:10.53391/mmnsa.1320914

EN

Generative adversarial network for load data generation: Türkiye energy market case

Abstract

Load modeling is crucial in improving energy efficiency and saving energy sources. In the last decade, machine learning has become favored and has demonstrated exceptional performance in load modeling. However, their implementation heavily relies on the quality and quantity of available data. Gathering sufficient high-quality data is time-consuming and extremely expensive. Therefore, generative adversarial networks (GANs) have shown their prospect of generating synthetic data, which can solve the data shortage problem. This study proposes GAN-based models (RCGAN, TimeGAN, CWGAN, and RCWGAN) to generate synthetic load data. It focuses on Türkiye's electricity load and generates realistic synthetic load data. The educated synthetic load data can reduce prediction errors in load when combined with recorded data and enhance risk management calculations.

Keywords

Load in Türkiye energy market, generative adversarial networks, synthetic data generation, unsupervised learning, RCGAN, TimeGAN, CWGAN, RCWGAN

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Details

Primary Language

English

Subjects

Numerical and Computational Mathematics (Other), Financial Mathematics

Journal Section

Research Article

Authors

Bilgi Yılmaz ^*
0000-0002-9646-2757
Germany

Early Pub Date

June 30, 2023

Publication Date

June 30, 2023

Submission Date

April 18, 2023

Acceptance Date

June 23, 2023

Published in Issue

Year 2023 Volume: 3 Number: 2

DOI

https://doi.org/10.53391/mmnsa.1320914

IZ

https://izlik.org/JA79FE26JY

APA

Yılmaz, B. (2023). Generative adversarial network for load data generation: Türkiye energy market case. Mathematical Modelling and Numerical Simulation With Applications, 3(2), 141-158. https://doi.org/10.53391/mmnsa.1320914

AMA

1.Yılmaz B. Generative adversarial network for load data generation: Türkiye energy market case. MMNSA. 2023;3(2):141-158. doi:10.53391/mmnsa.1320914

Chicago

Yılmaz, Bilgi. 2023. “Generative Adversarial Network for Load Data Generation: Türkiye Energy Market Case”. Mathematical Modelling and Numerical Simulation With Applications 3 (2): 141-58. https://doi.org/10.53391/mmnsa.1320914.

EndNote

Yılmaz B (June 1, 2023) Generative adversarial network for load data generation: Türkiye energy market case. Mathematical Modelling and Numerical Simulation with Applications 3 2 141–158.

IEEE

[1]B. Yılmaz, “Generative adversarial network for load data generation: Türkiye energy market case”, MMNSA, vol. 3, no. 2, pp. 141–158, June 2023, doi: 10.53391/mmnsa.1320914.

ISNAD

Yılmaz, Bilgi. “Generative Adversarial Network for Load Data Generation: Türkiye Energy Market Case”. Mathematical Modelling and Numerical Simulation with Applications 3/2 (June 1, 2023): 141-158. https://doi.org/10.53391/mmnsa.1320914.

JAMA

1.Yılmaz B. Generative adversarial network for load data generation: Türkiye energy market case. MMNSA. 2023;3:141–158.

MLA

Yılmaz, Bilgi. “Generative Adversarial Network for Load Data Generation: Türkiye Energy Market Case”. Mathematical Modelling and Numerical Simulation With Applications, vol. 3, no. 2, June 2023, pp. 141-58, doi:10.53391/mmnsa.1320914.

Vancouver

1.Bilgi Yılmaz. Generative adversarial network for load data generation: Türkiye energy market case. MMNSA. 2023 Jun. 1;3(2):141-58. doi:10.53391/mmnsa.1320914