In microgrids, energy management systems (EMS) have been considered essential systems to optimize energy scheduling, control and operation for reliable power systems. Conventional EMS researches have been predominantly performed by employing demand-side management and demand response (DR). Nonetheless, multi-action control in EMS is confronted with operational challenges in terms of the profitability and stability. In this paper, energy information systems (EIS), energy storage systems (ESS), energy trading risk management systems (ETRMS), and automatic DR (ADR) are integrated to efficiently manage the profitability and stability of the whole EMS by optimal energy scheduling. The proposed microgrid EMS architecture is optimized by using proximal policy optimization (PPO) algorithm, which has been known to have good performance in terms of learning stability and complexity. A novel performance metric, represented as a burden of load and generation (BoLG), is proposed to evaluate the energy management performance. The BoLG is incorporated into the reward settings for optimizing the management of multi-action controls such as load shifting, energy charging-discharging, and transactions. From the simulation results, it is confirmed that the proposed architecture can improve energy management performance with the proper trade-off between stability and profitability, compared to dynamic programming (DP)-based and double deep Q-network (DDQN)-based operation.

Energy management systems (EMSs) are regarded as essential components within smart grids. In pursuit of efficiency, reliability, stability, and sustainability, an integrated EMS empowered by machine learning (ML) has been addressed as a promising solution. A comprehensive review of current literature and trends has been conducted with a focus on key areas, such as distributed energy resources, energy management information systems, energy storage systems, energy trading risk management systems, demand-side management systems, grid automation, and self-healing systems. The application of ML in EMS is discussed, highlighting enhancements in data analytics, improvements in system stability, facilitation of efficient energy distribution and optimization of energy flow. Moreover, architectural frameworks, operational constraints, and challenging issues in ML-based EMS are explored by focusing on its effectiveness, efficiency, and suitability. This paper is intended to provide valuable insights into the future of EMS.

The transition to smart grids has served to transform traditional power systems into data-driven power systems. The purpose of this transition is to enable effective energy management and system reliability through an analysis that is centered on energy information. However, energy theft caused by vulnerabilities in the data collected from smart meters is emerging as a primary threat to the stability and profitability of power systems. Therefore, various methodologies have been proposed for energy theft detection (ETD), but many of them are challenging to use effectively due to the limitations of energy theft datasets. This paper provides a comprehensive review of ETD methods, highlighting the limitations of current datasets and technical approaches to improve training datasets and the ETD in smart grids. Furthermore, future research directions and open issues from the perspective of generative AI-based ETD are discussed, and the potential of generative AI in addressing dataset limitations and enhancing ETD robustness is emphasized.

Energy theft causes a lot of economic losses every year. In the practical environment of energy theft detection, it is required to solve imbalanced data problem where normal user data are significantly larger than energy theft data. In this paper, a variational autoencoder-generative adversarial network (VAE-GAN)-based energy theft-detection model is proposed to overcome the imbalanced data problem. In the proposed model, the VAE-GAN generates synthetic energy theft data with the features of real energy theft data for augmenting the energy theft dataset. The obtained balanced dataset is applied to train a detector which is designed as one-dimensional convolutional neural network. The proposed model is simulated on the practical dataset for comparing with various generative models to evaluate their performance. From simulation results, it is confirmed that the proposed model outperforms the other existing models. Additionally, it is shown that the proposed model is also very useful in the environments of extreme data imbalance for a wide variety of applications by analyzing the performance of detector according to the balance rate.

The accurate segmentation of cracks in structural materials is crucial for assessing the safety and durability of infrastructure. Although conventional segmentation models based on deep learning techniques have shown impressive detection capabilities in these tasks, their performance can be restricted by small amounts of training data. Data augmentation techniques have been proposed to mitigate the data availability issue; however, these systems often have limitations in texture diversity, scalability over multiple physical structures, and the need for manual annotation. In this paper, a novel generative artificial intelligence (GAI)-driven data augmentation framework is proposed to overcome these limitations by integrating a projected generative adversarial network (ProjectedGAN) and a multi-crack texture transfer generative adversarial network (MCT2GAN). Additionally, a novel metric is proposed to evaluate the quality of the generated data. The proposed method is evaluated using three datasets: the bridge crack library (BCL), DeepCrack, and Volker. From the simulation results, it is confirmed that the segmentation performance can be improved by the proposed method in terms of intersection over union (IoU) and Dice scores across three datasets.

In industrial Internet of Things (IIoT) systems, imbalanced datasets are prevalent because of the relative ease of acquiring normal operational data compared to abnormal or faulty data. An unbalanced distribution of data may lead to a biased learning problem, resulting in performance degradation of deep learning models. Data augmentation approaches based on generative adversarial networks (GAN) have been proposed to mitigate biased learning problems. However, GAN-based approaches constructed solely with convolutional neural networks may be incapable of extracting temporal properties from data. To utilize the temporal properties of data, a novel GAN structure consisting of an embedding network and recurrent neural networks is proposed in this paper. Additionally, in the novel GAN model based on mean-squared error, modified loss and mutual information terms are employed to improve training stability. From simulation results, it is confirmed that classification accuracy can be significantly improved by up to 54% based on the proposed method when compared with conventional fault diagnosis methods.