Angelo Cesar Colombini

Spatial Big Data mining is often hindered by high computational complexity and the intrinsic autocorrelation of georeferenced records. To address these challenges, this study proposes an architectural optimization framework for the CHSMST+ algorithm, designated as CHSMST+MR. Rather than introducing a brand-new clustering paradigm, the framework focuses on a Distributed Spatial Cardinality Reduction (DSCR) layer that aggregates redundant spatial records before the core iterative mining logic begins. By transforming raw records into a weighted key-value representation within the Apache Spark environment, the proposed approach significantly mitigates the shuffling bottleneck common in distributed systems. Experimental validation using high-density biological datasets demonstrates an average execution-time reduction of 51.36%, with performance gains reaching up to 79.96% in specific high-redundancy scenarios. The results, obtained through controlled local emulation, confirm that this architectural optimization provides a scalable, deterministic, and lossless solution for accelerating spatial clustering. This work contributes a methodological path for enhancing the performance of iterative spatial mining algorithms in environments characterized by massive data density and coordinate redundancy.

This work presents an experimental investigation of the effects of aging on the photometric performance of public luminaires equipped with LED technology. Four commercial models with powers of 40W, 80W, 150W, and 200W were evaluated, subjected to standardized tests under controlled laboratory conditions. The tests followed the IES LM-80-15 standards and INMETRO regulations, and included photometric analyses, goniometric tests, UV radiation tests, and In-Situ thermal measurements (ISTMT). The results reveal significant variations in luminous flux, energy efficiency, and light quality over six thousand hours of simulated operation. Optical degradation, especially of the lenses, showed a direct impact on the uniformity of light distribution. The study provides relevant technical support for the specification, acquisition, and maintenance of public luminaires, contributing to the increase of the lifespan and efficiency of the urban lighting system.

The building and construction sectors account for over one-third of global final energy consumption and nearly 40% of total direct and indirect CO2 emissions. At the same time, hospital buildings are often among the least energy-efficient. Enhancing the energy performance of existing hospital buildings through retrofitting measures presents a significant opportunity for cost and energy savings. However, hospitals have unique challenges, such as continuous occupancy, heavy medical equipment, and strict safety regulations. Additionally, the lack of financial incentives and supportive policies are among the biggest barriers to retrofitting hospital facilities. This paper proposes an energy management methodology for selecting the best retrofitting measures for hospital buildings. Moreover, the study seeks to define a priority ranking for the energy efficiency measures selected in an energy retrofit project, distinguishing between primary and supplementary actions. The methodology includes a generic process flow diagram, a systematic flowchart to facilitate decision-making, and two tables outlining primary and supplementary retrofitting measures.

The objective of this study is to investigate the probabilities of solar resource availability and the reliability of photovoltaic systems connected to Brazil's National Interconnected System (SIN). The research examines the uncertainties associated with these resources and their impact on the stability of the SIN. In order to achieve this, data science methods, including K-means clustering and empirical cumulative distribution curves (ECDF and ECCDF), were employed in order to conduct a comprehensive probabilistic analysis. The results indicate that, on a typical day, the probability of average monthly solar irradiation being less than 4,000 Wh/m².day is essentially zero, thereby reinforcing the reliability of the solar resource. Moreover, an examination of the data for August 15, 2023, revealed no notable correlation between the availability of the solar resource and the failure of the SIN. This study contributes to a more comprehensive understanding of renewable energy sources in Brazil and to the formulation of energy planning strategies that ensure the stability of the SIN in the context of the growing penetration of renewable sources.

This work presents a case study of the application of supervised and unsupervised machine learning techniques in the study of three of Petrobras FPSO units in regards to its equipments power demand. Specifically, it delves into the outcomes of the clustering and equipment modelling modules of a computational solution developed in a partnership between Universidade Federal Fluminense (UFF) and Petrobras, FPSO Power Demand Analytics (FPDA). The presented results were found satisfactory by UFF and Petrobras development and engineering teams. For example the equipment modelling methodology resulted in a library of models from which the median absolute error rarely exceeds the 3% mark. The median of the median absolute errors observed across platforms and test scenarios is often less than 1%.

The popularization of the internet allowed the use of different technologies for storing and retrieving bilingual dictionaries. An example is the Resource Description Framework (RDF) datasets. The storage of bilingual dictionaries in this type of structure makes it possible to discover indirectly connected translations through inference. However, such datasets are not efficient in graph search operations, which are important actions for inference techniques in the discovery of translations. On the other hand, database management systems based on the graph model offer a collection of resources that facilitate the tasks that involve such operations. In this way, a group of bilingual dictionaries from an RDF dataset was adapted to a Graph Database Management System (GDMS) which made it possible to reduce and simplify the structures of these dictionaries. Subsequently, 2 translation inference techniques were implemented in which two new bilingual English-Portuguese dictionaries were automatically generated. Thus, such inference algorithms are efficient in generating new dictionaries through GDMS. The scientific contribution is the application of a new technology for data storage and retrieval in support of bilingual dictionaries, in which there are advantages together with inference techniques in the generation of bilingual dictionaries and not addressed by related papers in literature.

RESUMO ESTRUTURADO Este trabalho aborda os conceitos e as implicações da utilização da geração distribuída (GD) fotovoltaica nas redes elétricas de média e baixa tensão. São avaliados os possíveis impactos causados aos reguladores de tensão ao longo destas redes. A análise considerou a variação do nível de geração das GDs em 8 cenários. Nestes cenários serão avaliadas a operação de comutação de TAP em um regulador de linha de distribuição, uma indústria e por último a análise da Fase A de três reguladores. Para isso foram utilizadas simulações através do programa computacional OpenDSS. O primeiro cenário é realizado sem a penetração de GDs e os demais com a penetração das GDs de 50 % e 90 %. Tais análises foram realizadas utilizando o caso teste 8.500 barras do IEEE (Institute of Electrical and Electronics Engineers), disponível na biblioteca do OpenDSS e os resultados são comentados para caracterizar os impactos na tensão da barra. Palavras-chave: Geração distribuída; Regulador de tensão; Estudo de casos; Intermitência fotovoltaica; Qualidade de energia; OpenDSS.

Since the 1980s, the Brazilian government has implemented many policies and programmes addressing energy efficiency. The electricity consumption in residential and commercial, service and public buildings in Brazil is approximately 50% of the total electricity consumed in the country; 70% of electricity consumption in public buildings is due to the lighting and air-conditioning systems. This paper presents a case study in a Brazilian Navy building, using DIALux software to study energy efficiency and to make an energy diagnosis regarding lighting to propose improvements so as to obtain the Brazilian partial National Energy Conservation Label (partial ENCE). Three different scenarios were simulated, and the results showed that it is possible to obtain about 2475 kWh/(m²/year) in energy savings, by replacing the current lighting system of fluorescent lamps with light-emitting diode (LED) lighting. Thus, it was possible to observe, through the results of the simulation and the energy diagnosis, that the replacement of the lighting technology, associated with layout modification, provides satisfactory results and meets the minimum desirable requirements of the Brazilian technical standard ABNT NBR ISO/CIE 8995-1, 2013.

This paper presents a big data analytics-based model developed for electric distribution utilities aiming to forecast the demand of service orders (SOs) on a spatio-temporal basis. Being fed by robust history and location data from a database provided by an energy utility that is using this innovative system, the algorithm automatically forecasts the number of SOs that will need to be executed in each location in several time steps (hourly, monthly and yearly basis). The forecasted emergency SOs demand, which is related to energy outages, are stochastically distributed, projecting the impacted consumers and its individual interruption indexes. This spatio-temporal forecasting is the main input for a web-based platform for optimal bases allocation, field team sizing and scheduling implemented in the eleven distribution utilities of Energisa group in Brazil.

This paper introduces a methodology for modelling a digital admittance-type distance relay using PSCAD/EMTDC. The proposed distance relay was tested in a simulation of the Brazilian power grid with predetermined fault scenarios. The goal of this paper is to make a detailed evaluation of the mho distance relay. The main aspects include the correct operation of the distance relay, fault resistance effects on the mho characteristics, and the fault detection time of this relay. A new approach to analyse the fault detection time is presented, considering several simulated fault scenarios. The results demonstrate that the fault resistance influences the fault detection time and severely affects the distance relay’s general performance. The fault detection time is not constant. It varies within a time interval, considering different fault types, fault locations, and fault resistances. The confidence interval calculation provides a detailed range of the fault detection time, considering its upper and lower limits.