PPGEE https://repositorio.ufpb.br/jspui/handle/tede/4457 2026-11-01T23:43:35Z 2026-11-01T23:43:35Z Oliveira, Izabelly Soares de https://repositorio.ufpb.br/jspui/handle/123456789/39575 2026-10-20T06:11:50Z 2024-05-24T00:00:00Z

Título: Reconfiguração de redes de distribuição de energia elétrica utilizando Algoritmo de Busca de Corvo Modificado Autor(es): Oliveira, Izabelly Soares de Orientador: Rodriguez, Yuri Percy Molina Abstract: This work presents a new alternative to solve the problem of Reconfiguration of Electricity Distribution Networks (RDEE), using the Crow Search Algorithm (ABC), in its modified format, which is a method based on the intelligent behavior of the crow bird. The proposed algorithm aims to minimize power losses that occur in distribution networks, taking into account their operational limits. For this, a program was created in the Python programming language, making use of the resources of software Open Distribution System Simulator (OpenDSS), allowing integration between OpenDSS and the proposed reconfiguration method. The ABC-M parameters were selected based on simulations that proved the best values to be considered when applying the algorithm. Finally, the proposed algorithm was implemented in a 33-bus distribution system and compared to results from other modern techniques reported in the literature. Thus, this work rea!rms evidence of the e!ciency of the proposed method in applying the reconfiguration problem. Editor: Universidade Federal da Paraíba Tipo: Dissertação

2024-05-24T00:00:00Z Froes, Filipe Bulhões https://repositorio.ufpb.br/jspui/handle/123456789/39397 2026-10-03T06:10:26Z 2026-01-30T00:00:00Z

Título: Análise e desenvolvimento de modelos de machine learning para simulação de uma usina termelétrica de ciclo combinado Autor(es): Froes, Filipe Bulhões Orientador: Macêdo, Euler Cássio Tavares de Abstract: Artificial intelligence (AI) has proven to be a powerful tool for addressing the challenges of energy transition and the search for greater efficiency in power generation systems. In the Brazilian electricity sector, marked by the growing participation of natural gas-fired thermoelectric power plants, machine learning (ML) techniques can help optimize the operation of these plants, contributing to greater efficiency and reduced emissions. In this context, digital twin systems (DTS) emerge as a promising technology for simulation and decision support. The objective of this study was to develop and compare different machine learning models applied to the simulation of a combined cycle thermal power plant, in preparation for its integration into a DTS. Data processing, training, evaluation, and model comparison methodologies were explored, including individual algorithms and ensemble techniques such as bagging, boosting, stacking, and blending. The results obtained demonstrated that regression models such as SVR and GPR, as well as their variants in ensemble techniques, achieved better performance in terms of normalized mean absolute error and coefficient of determination. In addition, it was found that combinations of models can provide greater robustness and accuracy, indicating that hybrid approaches are promising alternatives for representing the behavior of the plant under different conditions. Editor: Universidade Federal da Paraíba Tipo: Dissertação

2026-01-30T00:00:00Z Aires, Virgínia Vieira https://repositorio.ufpb.br/jspui/handle/123456789/39391 2026-10-03T06:10:10Z 2026-02-27T00:00:00Z

Título: Study and implementation of a SmartTree in the IoT context aiming at real-time meteorological data measurement Autor(es): Aires, Virgínia Vieira Orientador: Souza, Cleonilson Protasio de Abstract: Given the growing need for innovative and accessible environmental monitoring solutions, especially in hard-to-reach areas, this study investigates the use of temperature sensors installed on a structure inspired by natural trees, referred to as the SmartTree, located at the Center for Alternative and Renewable Energies (CEAR) of the Federal University of Paraíba, Brazil, to estimate meteorological parameters such as wind speed and direction. Motivated by this context, the study proposes an alternative approach to conventional meteorological monitoring by employing tree inspired structures as natural platforms for climate sensing, based on the hypothesis that internal temperature variations within the tree trunk, captured by sensors installed at different heights, depths, and orientations, indirectly reflect external patterns of wind and ambient temperature. The methodology is based on the application of the Discrete Wavelet Transform to extract multiscale features, which are used as input for machine learning models, including Random Forest, Gradient Boosting, Support Vector Machine, and Linear Regression. Data were collected and complemented by additional information from a nearby weather station. Among the evaluated models, Random Forest achieved the best performance, reaching high accuracy with a mean absolute percentage error of 6.60% for wind direction. The results confirm the potential of the tree inspired solution as a viable alternative for climate monitoring, with important applications such as forest fire prevention. Editor: Universidade Federal da Paraíba Tipo: Dissertação

2026-02-27T00:00:00Z Matos, Vinício de Araújo https://repositorio.ufpb.br/jspui/handle/123456789/38655 2026-08-12T06:12:19Z 2025-12-08T00:00:00Z

Título: Análise e modelagem das emissões instantâneas de co2 em veículos de combustão interna por meio de dados OBD-II e redes neurais artificiais Autor(es): Matos, Vinício de Araújo Orientador: Villanueva, Juan Moises Maurício Abstract: This study develops a methodology to evaluate and model vehicle carbon dioxide (CO2) emissions using data collected via the OBD-II interface, integrating detailed automotive analysis with artificial neural network techniques. The overall goal is to compare the emission behavior of internal combustion engine vehicles with electric vehicles under real operating conditions. Relevant operational data (speed, throttle position, air flow, etc.) were gathered directly from vehicles through OBD-II and then explored to identify patterns and correlations (such as the strong relationship between engine load and CO2 generation). Predictive models using Multi-Layer Perceptron (MLP) and Long Short-Term Memory (LSTM) neural networks were trained to estimate instantaneous CO2 emissions. The LSTM model achieved the best performance with a mean absolute percentage error (MAPE) of around 5%, significantly outperforming the MLP (MAPE 16%). The comparative analysis indicates that electric vehicles eliminate direct emissions during use, but their total CO2 impact depends on the electrical grid mix and manufacturing emissions. In conclusion, the proposed approach - combining OBD-II data and neural networks - proved effective for real-time emission estimation and can complement standardized lab-based estimates. Editor: Universidade Federal da Paraíba Tipo: Dissertação

2025-12-08T00:00:00Z