This study provides a scientometric analysis of research focused on energy theft detection and load profiling in smart grid networks. Data were retrieved from the Web of Science and Scopus databases, covering publications from 2003 to April 2024. Using the Bibliometrix package and VOSviewer software, we analyzed trends in publications, author productivity, collaborative networks, and key journals. The study highlights significant growth in the research field, with China and the USA emerging as the most productive countries, with strong international collaboration. Nadeem Javaid is identified as a leading author, contributing to publications with a strong focus on the application of deep learning techniques for energy consumption analysis in smart grids. Key journals such as IEEE Access, Applied Energy, and Energies were found to be central to this research area. Our findings highlighted the importance of this area, as smart grid technologies continue to evolve, requiring advanced methodologies to detect non-technical losses and analyze consumption patterns. This research supports the United Nations’ (UN) Sustainable Development Goals (SDGs), particularly goals related to sustainable energy and infrastructure development, by emphasizing the importance of technological innovation and collaboration in tackling energy theft.

Cybersecurity in radio frequency (RF) technologies has become a critical concern, driven by the expansion of connected systems in urban and industrial environments. Although research on wireless networks and the Internet of Things (IoT) has advanced, comprehensive studies that provide a global and integrated view of cybersecurity development in this field remain limited. This work presents a scientometric and systematic review of international publications from 2009 to 2025, integrating the PRISMA protocol with semantic screening supported by a Large Language Model to enhance classification accuracy and reproducibility. The analysis identified two interdependent axes: one focusing on signal integrity and authentication in GNSS systems and cellular networks; the other addressing the resilience of IoT networks, both strongly associated with spoofing and jamming, as well as replay, relay, eavesdropping, and man-in-the-middle (MitM) attacks. The results highlight the relevance of RF cybersecurity in securing communication infrastructures and expose gaps in widely adopted technologies such as RFID, NFC, BLE, ZigBee, LoRa, Wi-Fi, and unlicensed ISM bands, as well as in emerging areas like terahertz and 6G. These gaps directly affect the reliability and availability of IoT and wireless communication systems, increasing security risks in large-scale deployments such as smart cities and cyber–physical infrastructures.

This study provides a scientometric analysis of research focused on energy theft detection and load profiling in smart grid networks. Data were retrieved from the Web of Science and Scopus databases, covering publications from 2003 to April 2024. Using the Bibliometrix package and VOSviewer software, we analyzed trends in publications, author productivity, collaborative networks, and key journals. The study highlights significant growth in the research field, with China and the USA emerging as the most productive countries, with strong international collaboration. Nadeem Javaid is identified as a leading author, contributing to publications with a strong focus on the application of deep learning techniques for energy consumption analysis in smart grids. Key journals such as IEEE Access, Applied Energy, and Energies were found to be central to this research area. Our findings highlighted the importance of this area, as smart grid technologies continue to evolve, requiring advanced methodologies to detect non-technical losses and analyze consumption patterns. This research supports the United Nations’ (UN) Sustainable Development Goals (SDGs), particularly goals related to sustainable energy and infrastructure development, by emphasizing the importance of technological innovation and collaboration in tackling energy theft.

The occurrence of missing values in time series is a recurring problem caused by sensor failures or communication losses, compromising the quality of data-driven analyses. This work investigates the influence of Pix2Pix Generative Adversarial Network (GAN) hyperparameters on the imputation of missing values under different missing data mechanisms. Using the Electricity Load Diagrams 2011-2014 dataset, 40% of the observable values were artificially removed according to the Missing at Random (MAR), Missing Completely at Random (MCAR), and hybrid (MAR+MCAR) mechanisms. The Design of Experiments (DOE) approach was employed to systematically evaluate the effect of the Pix2Pix GAN hyperparameters on the Adjusted Symmetric Mean Absolute Percentage Error (ASMAPE). In addition to identifying the combination of learning rate and number of steps as the most influential hyperparameters in imputation performance, the results showed that the model maintains similar performance under different missing data mechanisms, highlighting the robustness and reliability of the Pix2Pix GAN in imputation tasks. The study contributes a statistically grounded methodology for hyperparameter analysis in energy consumption time series.

O objetivo do artigo é apresentar uma proposta de plano de ensino com foco na interdisciplinaridade, em uma única disciplina, e nas relações teórico-práticas, para a disciplina de Laboratório de Instalações Elétricas em um curso técnico em Eletrotécnica integrado ao ensino médio, de uma instituição pública federal de ensino localizada na região Centro-Oeste do estado de Minas Gerais. Para tanto, foi realizada a análise de sua ementa e conteúdo programático, com o objetivo de mapear as demais disciplinas do curso que dão suporte à disciplina de instalações elétricas, de forma a determinar as relações teórico-práticas que norteiam a formação do estudante e do futuro profissional em eletrotécnica e, assim, elaborar a reestruturação da disciplina de forma que promova a interdisciplinaridade.

Understanding the structure of electricity consumption data and exploring possible differences between normal and fraudulent behaviors are important steps toward supporting future proposals for energy theft detection systems. In this article, an exploratory data analysis was conducted on two randomly selected consumers (one normal and one fraudulent) from the State Grid Corporation of China dataset, one of the most widely used datasets for studying electricity theft. The analysis aimed to uncover latent consumption patterns by applying the autocorrelation function, correlation matrices, Shannon entropy analysis, weekly boxplot analysis, and periodograms to the two consumers described above. Additionally, the silhouette index was computed over the entire dataset to assess cluster cohesion and structure. Dataset labels were used solely for comparative analysis, with no intention of training or evaluating classification or prediction models. While the analysis is limited to a small sample, initial results suggest that, in this specific case, the fraudulent profile exhibited lower variability, weaker temporal and spectral structures, and lower entropy as well. The silhouette analysis showed a weak intra-cluster cohesion for the fraudulent consumers. These preliminary findings, though not generalizable, may inform future approaches in preprocessing, feature engineering, and anomaly understanding in smart grid contexts.