Estimating utility demand remains a significant challenge worldwide, being accuracy often compromised by numerous variables involved and limited relevant data available; this compromises models and impacts resource planning, infrastructure, and energy purchases. Energy systems must prioritize efficient resource utilization to address these challenges in the context of technological advances, economic changes, and environmental concerns. This paper conducts a bibliometric and systematic review of energy forecasting methods; the literature review covers the main studies conducted, including the most commonly used variables in forecasting studies, the techniques used, and the forecasting time horizon. As a result, the review presented here will facilitate the selection of the best models, variables, and time horizons for different forecasting applications.

Currently, for analyzing harmonic impacts on voltage at a point of interest, due to multiple nonlinear loads, the literature recommends carrying out simultaneous and synchronized measurement campaigns in all suspicious points with the use of high cost energy quality analyzers that are usually not available at the customers’ facilities and very often also not at the electric utilities. To overcome this drawback this paper proposes a method of assessing the harmonic impact due to multiple nonlinear loads on the total voltage harmonic distortion using only the load current true RMS values which are already available in all customers’ installations. The proposed methodology is based on Regression Tree technique using the Permutation Importance indicator which is validated in two case studies using two different electrical systems. The first case study is to ratify the use of Permutation Importance to measure the impact factor of each nonlinear load in a controlled scenario, the IEEE-13 bus test system, using ATP simulation (Alternative Transient Program). The second is to apply the methodology to a real system, an Advanced Measurement Infrastructure System (AMI) implanted on a campus of a Brazilian University, using low cost meters with only true RMS current measurements. The results achieved demonstrated the feasibility of applying the proposed methodology in real electric systems without the need for additional investments in high-cost energy quality analyzers.

In the electricity market, the issue of contract negotiation prices between generators/traders and buyers is of particular relevance, as an accurate contract modeling leads to increased financial returns and business sustainability for the various participating agents, encouraging investments in specialized sectors for price forecasting and risk analysis. This paper presents a methodology applied in experiments on energy forward curve scenarios using a set of techniques, including Long Short-Term Memory (LSTM), Extreme Gradient Boosting (XGBoost), Seasonal AutoRegressive Integrated Moving Average with eXogenous regressors (SARIMAX), and Feature Engineering to generate a 10-year projection of the Conventional Long-Term Price. The model validation proved to be effective, with errors of only 4.5% by Root Mean Square Error (RMSE) and slightly less than 2% by Mean Absolute Error (MAE), for a time series spanning from 7 January 2012 to 31 August 2024, in the Brazilian energy market.

The purpose of this article is to develop a methodology to apply to multi-objective optimization algorithms aimed at energy efficiency in buildings, considering aspects such as incremental cost, energy consumption, greenhouse gas emissions and energy efficiency levels of lighting and air conditioning system, according to the mandatory technical regulation in public buildings in Brazil. Presenting a solution to assist in the decision making of engineers, architects or building managers for the optimal arrangements’ choice for lighting and air conditioning equipment, considering each built environment and project profile. For the validation process, a basic building was created with 15 rooms spread over three floors, according to the most common construction parameters in the North of Brazil. First, different combinations of objective-function candidates were investigated to compose the multi-objective algorithm fitness function, analyzing its performance in two central scenarios: (1) adding some “baits” in air conditioning equipment files, and (2) without this inclusion. Thus, it was found that considering only three objective functions—incremental cost, energy consumption and the air conditioning energy efficiency coefficient—it is possible to get optimal non-dominated solutions in both scenarios, thus highlighting the robustness of the proposed methodology.

Este artigo aborda uma ação extensionista universitária realizada em uma comunidade quilombola localizada no Baixo Amazonas, Território Trombetas, comunidade Aracuan de Baixo, e suas resultantes. A iniciativa teve como objetivo principal promover a integração tecnológica e educacional por meio de diversas atividades, como oficina de STEM, capacitação de professores da rede básica e criação de jogos, também promover o ingresso de quilombolas na universidade. Como resultados, inclui-se os relatos dos participantes, destacando os benefícios proporcionados pela ação, tanto para os alunos quanto para a comunidade em geral, e as fotos do evento. Os depoimentos evidenciam o impacto positivo dessas intervenções na autoestima, na busca por conhecimento e no fortalecimento dos laços entre os envolvidos, além de ressaltar a importância de iniciativas semelhantes para o desenvolvimento educacional na Amazônia.

As Edificações consomem anualmente cerca de 50% da energia elétrica consumida na matriz energética nacional. As maiores cargas elétricas presentes nas Edificações estão nos sistemas de condicionamento de ar, iluminação e aquecimento. O objetivo deste artigo é aplicar uma metodologia embasada em algoritmos de otimização multiobjetivo visando a eficiência energética em edificações, de acordo com Instrução Normativa Inmetro para edificações comerciais, de serviços e públicas - INI-C. Apresentando-se, assim, como solução para auxiliar na tomada de decisão dos engenheiros, arquitetos e gestores de edificações para a escolha de arranjos ótimos de equipamentos de iluminação e climatização, ponderando sobre cada ambiente e perfil de projeto. A validação da metodologia deu-se em uma edificação real: o prédio da Pró-Reitoria de Administração da Universidade Federal do Pará com os parâmetros construtivos da região Norte do Brasil. A etapa de validação atestou a eficácia e robustez da metodologia proposta em um caso real, encontrando uma economia anual no custo energético em R$ 40.506,73 e nas emissões de gases do efeito estufa de 4.932,18 tonCO2/ano na solução apresentada pelo fitness. A simulação no caso real resultou em 112 possibilidades ótimas, permitindo ao usuário a escolha do arranjo mais apropriado ao perfil do projeto desejado.