Wind energy has become a trend in Brazil, particularly in the northeastern region of the country. Despite its advantages, wind power generation has been hindered by the high volatility of exogenous factors, such as weather, temperature, and air humidity, making long-term forecasting a highly challenging task. Another issue is the need for reliable solutions, especially for large-scale wind farms, as this involves integrating specific optimization tools and restricted-access datasets collected locally at the power plants. Therefore, in this paper, the problem of forecasting the energy generated at the Praia Formosa wind farm, an eco-friendly park located in the state of Ceará, Brazil, which produces around 7% of the state’s electricity, was addressed. To proceed with our data-driven analysis, publicly available data were collected from multiple Brazilian official sources, combining them into a unified database to perform exploratory data analysis and predictive modeling. Specifically, three machine-learning-based approaches were applied: Extreme Gradient Boosting, Random Forest, and Long Short-Term Memory Network, as well as feature-engineering strategies to enhance the precision of the machine intelligence models, including creating artificial features and tuning the hyperparameters. Our findings revealed that all implemented models successfully captured the energy-generation trends, patterns, and seasonality from the complex wind data. However, it was found that the LSTM-based model consistently outperformed the others, achieving a promising global MAPE of 4.55%, highlighting its accuracy in long-term wind energy forecasting. Temperature, relative humidity, and wind speed were identified as the key factors influencing electricity production, with peak generation typically occurring from August to November.

Due to the need to create new technologies for the production of biofuels, there are techniques to perform oil extraction from oilseeds, such as the supercritical extraction method using CO2, where the seed used to carry out the study (peanut) is subjected to CO2 at high pressures and temperatures, thus becoming a supercritical fluid and carrying out the process of extracting the oil from the seed to produce biodiesel more efficiently. There is another extraction technique in which organic solvents are used. Still, this method has some difficulties that end up causing damage to the environment as it is a process that consumes a significant amount of time and energy. Peanut is the world's fourth-most cultivated oilseed, with large plantations in the Americas, Africa, and Asia. Its planting is carried out to produce grains, oil, and bran. CO2 has interesting physicochemical properties since it is an inert, non-polar, non-flammable, odorless, tasteless gas and has critical parameters and low value. It has a critical pressure of 72.01 bar and a critical temperature of 31.1 °C. The present work presents data from a kinetic study of the supercritical extraction of peanut oil under pressure and temperature conditions of 200 bar, 280 bar, 40 °C, and 60 °C. This project uses an experimental matrix to help carry out the experiments. At the end of the experiments, we obtained a yield of 30% in the supercritical method (80 min) and a percentage yield of 26% by Soxhlet using ethanol as a solvent for 480 minutes.

Esta pesquisa apresenta um estudo conceitual sobre as redes elétricas inteligentes (Smart Grid) e o gerenciamento de riscos de acordo baseado no guia Project Management Body of Knowledge (PMBOK) v. 6.0, assim como a elaboração de questionamentos acerca do gerenciamento de riscos. a metodologia aplicada foi uma survey utilizada para coleta de dados de profissionais de organizações do setor de energia elétrica que trabalham em projetos de redes elétricas inteligentes entregues ao setor privado. com a análise das respostas, concluiu-se que o planejamento do projeto pode ser considerado como uma fase crítica em relação ao gerenciamento de riscos do projeto, uma vez que pode apresentar maiores complicações caso não seja desenvolvida de forma satisfatória, prejudicando ainda o restante dos grupos de processos como a execução, o controle e monitoramento e o encerramento do projeto. além disso, as organizações em sua maioria realizam gerenciamento de riscos, mas nem sempre de forma frequente, completa e efetiva ou ainda utilizando técnicas e ferramentas altamente confiáveis e difundidas.

Organizado por: Prof.° Me. Myller Augusto Santos Gomes ISBN 978-65-5379-002-5 DOI: 10.47573/aya.5379.2.57 N° páginas: 72 Formato: Livro Digital (PDF)

Esta pesquisa sugere a elaboração de um projeto de iluminação pública (IP) eficiente, levando em conta a viabilidade técnico-financeira das luminárias que compõem o sistema de IP do município de Rosana. É apresentado o estudo para levantar os potenciais de economia no consumo de energia elétrica e no valor pago mensalmente com IP. O trabalho tem como finalidade a substituição das luminárias convencionais (a vapor de sódio) que tem vida mediana curta e maior consumo energético, por luminárias led (Light Emitting Diode) que tem um tempo de vida útil média maior e são mais eficientes. A análise econômica foi feita com base em indicadores padrões de viabilidade de investimentos, como valor presente líquido (VPL), payback descontado e taxa interna de retorno (TIR). por fim, apresenta análises de dados com o intuito de otimizar o sistema de ip e reduzir o consumo de energia elétrica no município de Rosana.

Objective: This study aims to investigate the relationship between climate change and electricity price fluctuations in the Brazilian free energy market, with a focus on drought periods. The goal is to demonstrate that there is a significant correlation between extreme weather events, such as prolonged droughts, and price behavior in this energy contracting environment. Theoretical Framework: Brazil’s electricity matrix is predominantly hydro-based, making it vulnerable to seasonal variations in rainfall. Both the regulated and the free energy markets are affected by hydrological fluctuations, albeit with distinct pricing mechanisms. This study draws on literature and institutional data related to energy pricing structures, climate change impacts, and the formulation of the Spot Price for Differences (PLD). Method: The methodology is based on bibliographic and documentary research, with data collected from official sources such as INMET (National Institute of Meteorology), ANEEL (National Electric Energy Agency), CCEE (Electric Energy Trading Chamber), and ONS (National Electric System Operator). The study analyzed data from 2020 to 2023, especially during the 2021 drought, covering reservoir levels, PLD variations, and energy tariff adjustments. Results and Discussion: The analysis shows a strong correlation between low reservoir levels and rising electricity prices in the free energy market, as evidenced by historical PLD peaks and significant tariff readjustments. These findings highlight the sector’s sensitivity to adverse weather conditions, even in a more flexible environment like the Free Contracting Market (ACL). Research Implications: Keeping electricity prices competitive is crucial for national economic performance, as price increases directly affect industrial competitiveness, cost of living, and energy security. The study emphasizes the importance of climate forecasting and energy planning as tools for mitigating economic and environmental risks. Originality/Value: This research contributes to the literature by demonstrating that the free energy market — although more dynamic and decentralized — is still highly vulnerable to climatic factors. The PLD, a key price indicator in the ACL, responds directly to changes in hydrological conditions and the need for more expensive and polluting thermal generation.