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The reports represent cutting-edge research with significant potential for major impact in the field. Features are submitted by individual invitation or referral from the Scientific Editors and are peer-reviewed before publication.

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The paper can be either an original scientific article, a substantial new study often involving several techniques or methods, or a comprehensive review paper with brief and precise updates of the latest advances in the field. n systematically review the most exciting developments in science. literature. This type of paper provides an overview of possible future research directions or applications.

Editor’s Choice articles are based on recommendations from scientific journal editors from around the world. The editors select a small number of articles recently published in the journal that they believe will be of particular interest to the authors or relevant to the field. The aim is to provide a snapshot of some of the most exciting developments published in the various research areas of the journal.

Received: 2 December 2021 / Revised: 10 January 2022 / Accepted: 19 January 2022 / Published: 24 January 2022

Energy consumption data is used to improve energy efficiency and reduce costs. However, there are two main challenges to obtaining energy consumption data: (i) data collection is very expensive, time-consuming, and (ii) consumer safety and privacy concerns that may be revealed by the actual data. In this research, we have addressed these challenges by using generative competition networks to generate an energy consumption profile. We were able to generate synthetic data that is similar to real energy consumption data. Based on recent research conducted on TimeGAN, we implemented a framework to generate synthetic energy consumption data that could be useful in research, data analysis, and business decision making. The framework is implemented using a real energy data set that includes 2020 energy consumption data for the Australian states of Victoria, New South Wales, South Australia, Queensland and Tasmania. Implementation results are evaluated using various performance measures and the results are displayed using visualization along with principal component analysis (PCA) and t-distributed stochastic neighbor embedding (TSNE) plots. Overall, the experimental results show that the synthetic data generated using the proposed implementation have very similar characteristics to the real data set with high comparison accuracy.

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Today’s modern energy system, or simply ‘smart grid’, is the result of advanced sensors, automation and control. In addition, the introduction of distributed renewable energy resources brought production closer to the consumer and introduced the concept of two-way energy flows. In order to effectively monitor and control smart grid systems, sensors and measuring devices are embedded throughout the network [1]. At the low voltage customer level, it was not well observed until recent years when the use of smart meters increased significantly. The data collected by smart meters have high security and privacy constraints as described by the authors in [2, 3], which is one of the reasons why there is a barrier to wider adoption, application and research for the energy industry. Similar to the wider adoption of smart meters, medium and high voltage power grids have used Phasor Measurement Units (PMU), Micro-PMUS and other sensors to improve grid performance and reliability. Medium to high voltage power grids are facing more challenges than before due to the increase of new types of cyber threats through information technology (IT) and operational technology (OT) vulnerabilities [4]. New types of cyberattacks are observed in the literature, for example, false data injection (FDI) attacks [5]. While one type of attack deals with the integrity of information or measurements, the other type of attack targets the availability of resources or information. A denial of service (DoS) attack is an example of such attacks. There is also a significant risk associated with concerns about the privacy of shared information. Consequently, privacy attacks have received considerable attention [6]. Privacy attacks can reveal the identity and usage behavior of users. If the data is for a large operational center, adversaries and competitors can use this information. Therefore, it was identified as an important question – “How can research and investigations be conducted without sharing the actual data that is vulnerable to security and privacy attacks?”

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A possible solution to this challenge is to generate synthetic energy data that is indistinguishable from real data that could be used for research purposes. given the underlying framework, this can be achieved by using generative competition networks [7, 8]. Therefore, in this study, we focused on the generation of synthetic energy data using a time series variant of Generating Adversarial Networks (GAN), which addresses research issues following in our research on synthetic data generation.

Generative adversarial networks (GANs) are a class of machine learning/deep learning framework used to train generative models. As the name suggests, the main function of a generative model is to generate an object from a given input. GAN was originally developed to create an image processing and image generation application that generates fake images as output from real images, given random noise as input to the model. The goal was to create a model that produced false images that were indistinguishable from the real image. Using a new solution of Generative adversarial networks, researcher Ian Goodfellow was able to create fake images and thus achieved the desired goal [9]. Many researchers have since built different variants of GANs using this core body of work. Some of the famous variants are StyleGAN, WGAN, ConditionalGAN, DCGAN, etc. Most of these generative networks are implemented on image datasets. This type of data does not show dynamic changes over time and has a simple or linear relationship with other variables. However, there are many areas of research where time is a valuable factor, in industrial applications such as energy, robotics, agriculture and medicine, researchers needed a historical data set that was recorded over a period of time. For such datasets, the GAN model must be able to capture the dynamics and variation of the data over time while preserving the complex relationship between the variables during the generation of the synthetic data. A time-varying GAN model usually consists of two traditional networks, a generator and a discriminator, and sometimes includes two additional networks called recovery and implantation, collectively known as autoencoders. This framework can be used to capture the temporal dynamics of time series data. Therefore, in this work, we focused on applying a basic GAN method to study data used by electric power equipment over a period of time and generate synthetic data similar to the real data of a dataset. The idea of ​​using energy consumption data is strongly influenced by the smart grid research done by authors based on energy data in [2, 10]. The objective of this work is to model a generative race network on energy consumption data and generate synthetic data that cannot be distinguished from the real data set in terms of data characteristics. This will allow us to create a new method to avoid privacy issues related to the use of customer data in future research that may be carried out in the field of energy.

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The organization of this work is as follows: Section 2 describes related work on energy and productive adversarial networks. Section 3 presents the research design and methodology along with the GAN framework. Section 4 describes the method and technical details of artifact development. It lists various parameters related to the GAN framework along with information about the dataset. Section 5 provides information on experimental setups. The implementation results are then discussed in Section 6. Finally, Section 7 concludes the study and explains how the designed artifact is suitable for solving real-world problems. We have added a table (Table 1) that gives a complete definition and form of all abbreviations.

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The concept of a generative model has recently attracted research interest. A large number of researchers have developed various new solutions using generative adversarial networks as a core model in various fields. There are many variants of the Generative adversarial network model that have been created to address the relevant problems. The concept of the GAN model, the philosophy behind the GAN framework, and the possible implementation of GANs are explained by Li, Yanchun et al. in [11].

Time series data are very different from other data sets mainly because of their dynamic fluctuations and the unknown location of the time series, which contributes a significant amount of parametric inference for non-stationary signals. Additionally, the dynamic row data distribution is unbalanced

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