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Synthetic Energy Data Generation Using Time Variant Generative Adversarial Network

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journal contribution
posted on 2022-01-01, 00:00 authored by S Asre, Adnan AnwarAdnan Anwar
Energy consumption data is being used for improving the energy efficiency and minimizing the cost. However, obtaining energy consumption data has two major challenges: (i) data collection is very expensive, time-consuming, and (ii) security and privacy concern of the users which can be revealed from the actual data. In this research, we have addressed these challenges by using generative adversarial networks for generating energy consumption profile. We have successfully generated synthetic data which is similar to the real energy consumption data. On the basis of the recent research conducted on TimeGAN, we have implemented a framework for synthetic energy consumption data generation that could be useful in research, data analysis and create business solutions. The framework is implemented using the real-world energy dataset, consisting of energy consumption data of the year 2020 for the Australian states of Victoria, New South Wales, South Australia, Queensland and Tasmania. The results of implementation is evaluated using various performance measures and the results are showcased using visualizations along with Principal Component Analysis (PCA) and t-distributed stochastic neighbor embedding (TSNE) plots. Overall, experimental results show that Synthetic data generated using the proposed implementation possess very similar characteristics to the real dataset with high comparison accuracy.

History

Journal

Electronics

Volume

11

Article number

355

Pagination

1-24

Location

Basel, Switzerland

Open access

  • Yes

ISSN

2079-9292

eISSN

2079-9292

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Issue

3

Publisher

MDPI AG