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

Bilgi Yilmaz
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引用次数: 1

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.
负载数据生成的生成对抗网络:t rkiye能源市场案例
负荷建模对于提高能源效率和节约能源至关重要。在过去的十年中,机器学习已经受到青睐,并在负载建模中展示了卓越的性能。然而,它们的实施在很大程度上依赖于可用数据的质量和数量。收集足够的高质量数据既耗时又极其昂贵。因此,生成式对抗网络(GANs)在生成合成数据以解决数据短缺问题方面显示出了其广阔的前景。本研究提出了基于gan的模型(RCGAN、TimeGAN、CWGAN和RCWGAN)来生成综合负荷数据。它专注于 rkiye的电力负荷,并生成真实的综合负荷数据。经教育的综合负荷数据与记录数据相结合,可以减少负荷预测误差,增强风险管理计算能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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