Synthetic demand data generation for individual electricity consumers : Generative Adversarial Networks (GANs)
- Load modeling is one of the crucial tasks for improving smart grids’ energy efficiency. Among many alternatives, machine learning-based load models have become popular in applications and have shown outstanding performance in recent years. The performance of these models highly relies on data quality and quantity available for training. However, gathering a sufficient amount of high-quality data is time-consuming and extremely expensive. In the last decade, Generative Adversarial Networks (GANs) have demonstrated their potential to solve the data shortage problem by generating synthetic data by learning from recorded/empirical data. Educated synthetic datasets can reduce prediction error of electricity consumption when combined with empirical data. Further, they can be used to enhance risk management calculations. Therefore, we propose RCGAN, TimeGAN, CWGAN, and RCWGAN which take individual electricity consumption data as input to provide synthetic data in this study. Our work focuses on one dimensional times series, and numerical experiments on an empirical dataset show that GANs are indeed able to generate synthetic data with realistic appearance.
Verfasser*innenangaben: | Bilgi YilmazORCiD, Ralf KornORCiD |
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URN: | urn:nbn:de:hbz:386-kluedo-70751 |
ISSN: | 2666-5468 |
Titel des übergeordneten Werkes (Englisch): | Energy and AI |
Verlag: | Elsevier |
Verlagsort: | Netherlands |
Herausgeber*in: | Donghan JinORCiD |
Dokumentart: | Wissenschaftlicher Artikel |
Sprache der Veröffentlichung: | Englisch |
Datum der Veröffentlichung (online): | 13.08.2022 |
Jahr der Erstveröffentlichung: | 2022 |
Veröffentlichende Institution: | Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau |
Datum der Publikation (Server): | 04.01.2023 |
Freies Schlagwort / Tag: | CWGAN; Generative adversarial networks; RCGAN; RCWGAN; Synthetic data generation; TimeGAN; Unsupervised learning |
GND-Schlagwort: | Electricity consumption |
Ausgabe / Heft: | Volume 9, August 2022, 100161 |
Seitenzahl: | 14 |
Quelle: | 10.1016/j.egyai.2022.100161 |
Fachbereiche / Organisatorische Einheiten: | Kaiserslautern - Fachbereich Mathematik |
DDC-Sachgruppen: | 5 Naturwissenschaften und Mathematik / 500 Naturwissenschaften |
MSC-Klassifikation (Mathematik): | 65-XX NUMERICAL ANALYSIS |
68-XX COMPUTER SCIENCE (For papers involving machine computations and programs in a specific mathematical area, see Section {04 in that areag 68-00 General reference works (handbooks, dictionaries, bibliographies, etc.) | |
Sammlungen: | Open-Access-Publikationsfonds |
Lizenz (Deutsch): | Zweitveröffentlichung |