Definitions of methodologies to regulate the quality of electricity supply services are a topic under active discussion in Brazil and worldwide. There are various ways to define limits and quality service goals. In Brazil, the regulation of limit indicators for consumer unit sets is carried out by the National Electric Energy Agency. Its latest revision took place in 2014, under the framework of Public Announcement No. 29/2014. The primary contribution of this research is the proposition of an artificial intelligence-assisted methodology, specifically utilizing machine-learning techniques capable of organizing and selecting the most relevant attributes for representing similar consumer sets. Tests were conducted with real data from the 2020 system. The results demonstrated that this methodology can select attributes from different categories, achieving data representativeness and clustering scores superior to those attained with attributes selected by the current ANEEL methodology. Furthermore, the proposed methodology exhibits greater replicability compared to the current approach. These outcomes contribute to the modernization of quality regulation in the electricity distribution sector, benefiting all stakeholders in the industry.

Maintenance activities on electric power distribution networks are an essential tool in achieving a robust electricity supply system that meets society's needs. Regulation indicators require distribution companies to invest in maintenance and frequently maintain their networks. However, current maintenance activities are executed using fossil fuel-powered trucks equipped with aerial baskets and without route planning, resulting in high travel costs. The advent of new technologies, such as electric trucks, raises questions about their feasibility in replacing fossil fuel-powered trucks. In this study, we propose a routing methodology for an electric truck equipped with an aerial basket that performs maintenance activities in electric power distribution networks, aiming to reduce operational costs. The methodology utilized is an extension of the electric travelling salesman problem with time windows, considering partial charging and work schedules. Tests were conducted in the cities of Mairiporã and Atibaia, in the state of São Paulo, analyzing the electric truck travel limits based on battery autonomy and the need for intermediate recharges. The analysis compares electric trucks and regular trucks using an implemented methodology. The results show that the shortest possible route was achieved, all planned activities were completed, and the need for intermediate recharges was identified

This article presents an innovative approach for efficiently extracting data from invoices and bills through the integration of image processing, YOLO (You Only Look Once), and OCR Tesseract. Initially, a preprocessing step can be performed to enhance the quality and uniformity of the documents. Next, object detection using YOLO is applied to identify regions of interest in the invoices and bills, such as value fields, dates, and identifiers. Subsequently, OCR Tesseract is employed to perform text extraction in these identified regions. Real data from judicial deposit slips of the Court of Justice of the State of Paraná (Brazil) were used to train and test the effectiveness of the method. Experimental results demonstrate the effectiveness of the proposed approach, achieving high data extraction accuracy rates, making it a promising solution for automating processes related to financial document management. In the study, a total of 1,000 images were submitted for identification, with a total processing time of 39 minutes and 55 seconds. The system successfully extracted 469 accounts, achieving a final prediction accuracy of 99.20%. Additionally, the method has been made available as a system on GitHub, facilitating its use and contributions by the developer community.

Este artigo propõe uma metodologia para análise de viabilidade técnica e econômica, para o fornecimento de rede privada 5G como serviço no mercado brasileiro, auxiliando o investidor na decisão para produção em escala comercial, com estudo de aplicação dentro do contexto de implementação da rede 5G no Brasil. Inicia-se o processo com a prospecção de usuário do segmento Business-to-Business (B2B), onde é realizado o levantamento das informações dos clientes, com base nestas informações é realizado o estudo de viabilidade técnica, buscando-se Estações rádio base (ERB’s) próximas para realizar o atendimento, na sequencia são levantados os custos de instalação, configuração e operação de uma rede privada 5G. Desenvolveu-se uma Demonstração do resultado do exercício (DRE) específica para este estudo de caso. Avalia-se então a viabilidade econômico-financeira do projeto para verificar se há rentabilidade e por meio de índices financeiros avalia-se inclusive uma viabilidade probabilista onde se analisa o mínimo cenário viável para comercialização. Ao final desta etapa, se realiza o estudo de caso do projeto com base nas premissas pré-estabelecidas durante a viabilidade técnica do projeto para o fornecimento de uma rede privada 5G. Após a implementação do projeto se sugere aplicações e planos de acordo com as necessidades do usuário final. O modelo comercial do serviço implementado se mostrou economicamente viável mesmo após adoção de cenários com alto investimento em Capex e Opex. Espera-se que contribua para o desenvolvimento de estratégias mais eficazes para o fornecimento de redes privadas 5G no mercado brasileiro de telecomunicações.

The process of optimizing the Brazilian hydrothermal system presents an extraordinary challenge, characterized by its continental scale, stochastic flows, and the non-convex nature of hydroelectric generation. Traditional models, based on aggregations of equivalent reservoirs and the linearization of generation functions, have struggled to provide appropriate operational policies in an increasingly complex energy landscape. Furthermore, due to this lack of adherence, the use of the current model as an electricity price setter and a tool for planning the expansion of the Brazilian electricity sector has been questioned by system participants. The Lynx Project introduces a new hierarchical structure designed to overcome these limitations. Its architecture is based on five premises: 1) individual modeling of each hydroelectric reservoir, using nonlinear generation functions to more accurately capture variations in head; 2) the use of fourth-degree polynomial functions to describe the relationships between head, reservoir area, and stored volume; 3) a multi-objective (cost-risk) optimization mechanism that minimizes both the expected operating cost and its respective standard deviation, solved through implicit stochastic optimization supported by the generation of hydrological scenarios; 4) a sophisticated stochastic input module, LYNX-Series, which employs a Contemporary Autoregressive Moving Average (CARMA) model and an original sampling method based on the Mahalanobis distance to select the most hydrologically relevant scenarios in order to reduce computational effort; and 5) an integrated Dynamic Optimal Power Flow (DOPF) model that verifies the feasibility of power dispatch in relation to the physical constraints of the power grid. The main contributions of the Lynx Project include the generation of a set of more appropriate operational policies reflecting different levels of risk aversion, as well as the creation of a powerful analytical tool capable of supporting high-risk strategic decisions in the Brazilian energy sector, including market restructuring and policy analysis.