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

Year 2023, , 141 - 158, 30.06.2023

Bilgi Yılmaz

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

Cited By: 2

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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