Andreia Antloga Do Nascimento

The transition to sustainable transportation is critical in the Amazon, where riverine routes are vital for mobility and commerce. The adoption of electric boats is hindered by limited charging infrastructure and unpredictable environmental conditions. To address this challenge, we present an integrated system that combines real-time sensor monitoring with machine learning-based range prediction to reduce operator range anxiety and enable intelligent trip planning. The system incorporates battery discharge modeling based on Peukert's Law and employs Gaussian Process Regression, which achieved superior performance (R² = 0.957, RMSE = 2.545) among evaluated methods through its balance of accuracy, computational efficiency, and uncertainty quantification. The system was validated on an electric boat operating on the Xingu River, Brazil, equipped with LiFePO4 batteries. The system provides boat operators with real-time dashboards displaying live navigation data, battery status, and reliable range estimates, significantly reducing operational uncertainty. This work demonstrates a practical solution for accurate range prediction in remote environments, advancing sustainable electric maritime mobility (green computing) in the Amazon, which can be applied to similar waterways worldwide.

The increasing reliance on electric vehicle (EV) charging in buildings requires balancing the load from other building systems to support the new demand. This paper uses a study case in a Near-Zero Energy Building (NZEB) educational facility located in the Brazilian Amazon to verify how much the energy efficiency (EE) measures would improve the existing ratings of the building and supply the expansion of EV demand. A comprehensive building energy load and energy performance analysis were conducted in four steps, following the mandatory Brazilian requirements for EE in public buildings, using measured data, computer modeling, and thermoenergetic analyses using OpenStudio version 1.1.0 and EnergyPlus software version 9.4.0. First, the EE retrofit measures were proposed and evaluated, targeting the air conditioning and lighting systems. Subsequently, an equation was elaborated to indicate the maximum level of energy consumption that could be increased without compromising the building’s energy performance and NZEB classification. Finally, Open DSS software version 10.0.0.2 was used to simulate the increased availability of EV charging after the retrofit. With the proposed retrofit, the building improved the EE ratings by three levels, and the percentage of the NZEB rating increased by 33.28%. These measures also increased the EV charging load by 20%, maintaining the maximum EE level and the NZEB classification, although EV maximization reduced self-sufficiency by 9.78% compared to the retrofit-only scenario.

The growth of electric vehicles (EVs) and their integration into existing and future buildings bring new considerations for energy efficiency (EE) and balance when combined with renewable energy. However, for buildings with an energy efficiency label, such as Near Zero Energy Building (NZEB) or Positive Energy Building (PEB), the introduction of EVs may result in the declassification of the EE label due to the additional energy required for the charging infrastructure. This underscores the increasing relevance of demand-side management techniques to effectively manage and utilize energy consumption and generation in buildings. This paper evaluates the influence of electric vehicle (EV) charging on NZEB/PEB-labeled buildings of the Brazilian Building Labeling Program (PBE Edifica). Utilizing on-site surveys, computational modeling, and thermos-energetic analysis with software tools such as OpenStudio v. 1.1.0 and EnergyPlus v. 9.4.0, an energy classification was conducted in a building in the city of Belem, State of Para, Brazil. Subsequently, power flow simulations employing probabilistic models and Monte Carlo approaches were executed in the OpenDSS software v. 10.0.0.2 to examine the impact of EV integration, both with and without the implementation of demand-side management techniques. Analyses using the labeling methodology demonstrated that the building has EE level C and NZEB self-sufficiency classification. The assessment of the impact of EV integration on the building’s total energy consumption in the base (current) scenario was carried out in two scenarios, with (2) and without (1) supply management. Scenario 01 generated a 69.28% increase in energy consumption, reducing the EE level to D and resulting in the loss of the NZEB class. Scenario 02 resulted in a smaller increase in energy consumption of 40.50%, and guaranteed the return of the NZEB class lost in scenario 1, but it was not enough to return the EE level to class C. The results highlight the need for immediate and comprehensive energy management strategies, as the findings show that the two scenarios present a difference of 41.55% in energy consumption. Nonetheless, these strategies are not enough if other consumption restrictions or energy efficiency measures are not applied to other building systems.

Objective: The aim of this study is to analyze charging strategies for electric vehicles (EVs) in a hybrid system connected to the conventional electricity grid, to optimize the use of renewable energies. Theoretical Framework: This paper presents the concepts of hybrid systems, photovoltaic power generation, and electric vehicle energy demand. It emphasizes the importance of integrating renewable energy into the transportation sector to reduce greenhouse gas emissions and promote sustainability. Method: The research was based on obtaining load curves and photovoltaic generation in different climatic conditions. The energy available for charging electric vehicles was calculated, considering the limitations of the hybrid system. Different charging power configurations were studied. Results and Discussion: The results demonstrate the feasibility of recharging EVs with renewable energy and highlight the operational flexibility provided by storage systems. Different climate scenarios affect energy availability for charging, highlighting the importance of adaptive strategies. Research Implications: This research contributes to developing sustainable strategies in the transportation sector by providing insights into integrating electric vehicles and renewable energy. Practical implications include reducing carbon emissions and decreasing dependence on fossil fuels. Originality/Value: This study stands out for its specific analysis of EV charging strategies in a hybrid context, providing new approaches for the efficient use of renewable energy in transportation. The integration of storage systems and adaptation to climatic conditions are innovative aspects that add value to the research.

Designing and deploying telecommunications and broadcasting networks in the challenging terrain of the Amazon region pose significant obstacles due to its unique morphological characteristics. Within low-power wide-area networks (LPWANs), this research study introduces a comprehensive approach to modeling large-scale propagation loss channels specific to the LoRaWAN protocol operating at 915 MHz. The objective of this study is to facilitate the planning of Internet of Things (IoT) networks in riverside communities while accounting for the mobility of end nodes. We conducted extensive measurement campaigns along the banks of Universidade Federal do Pará, capturing received signal strength indication (RSSI), signal-to-noise ratio (SNR), and geolocated point data across various spreading factors. We fitted the empirical close-in (CI) and floating intercept (FI) propagation models for uplink path loss prediction and compared them with the Okumura–Hata model. We also present a new model for path loss with dense vegetation. Furthermore, we calculated received packet rate statistics between communication links to assess channel quality for the LoRa physical layer (PHY). Remarkably, both CI and FI models exhibited similar behaviors, with the newly proposed model demonstrating enhanced accuracy in estimating radio loss within densely vegetated scenarios, boasting lower root mean square error (RMSE) values than the Okumura–Hata model, particularly for spreading factor 9 (SF9). The radius coverage threshold, accounting for node mobility, was 945 m. This comprehensive analysis contributes valuable insights for the effective deployment and optimization of LoRa-based IoT networks in the intricate environmental conditions of the Amazon region.

Diante da crescente preocupação com a preservação do meio ambiente, diversos projetos de sustentabilidade estão sendo implementados em todo o mundo. Entre esses projetos, destacam-se iniciativas relacionadas às mudanças climáticas e à redução das emissões de gases de efeito estufa. Nesse contexto, a Universidade Federal do Pará desempenha um papel importante ao funcionar como um laboratório vivo de mobilidade elétrica multimodal, por meio da integração de modais como ônibus e barco elétricos, sistemas de geração fotovoltaica, armazenamento de energia em baterias, carregadores elétricos de veículos, rede de comunicação para coleta de dados em tempo real, armazenamento em nuvem, além de hardwares e softwares. Através do Projeto do Sistema Inteligente Multimodal da Amazônia (SIMA), é possível avaliar se essa iniciativa consegue atender aos Objetivos de Desenvolvimento Sustentável (ODS), com o intuito de preservar o meio ambiente e o clima, ao mesmo tempo em que garante o progresso tecnológico e a prosperidade da população na região amazônica. Neste artigo se pretende avaliar como um laboratório vivo de mobilidade elétrica pode ser sustentável atendendo requisitos de sustentabilidade e melhoria da qualidade de vida.

A transformação climática do planeta causada pelo aquecimento global levou os principais países a assinarem tratados para diminuir a temperatura do planeta a partir de alternativas sustentáveis. Os projetos de mobilidade elétrica sustentáveis se apresentam como alternativa para atingir objetivos ambientais definidos. Com esse fim, em 2019, a Universidade Federal do Pará (UFPA), através do seu Plano de Desenvolvimento Institucional apoiou um Projeto de P&D Estratégico - Solução de Mobilidade Elétrica Eficiente, aprovado na Agência Nacional de Energia Elétrica, parceria da Norte Energia S.A com o Centro de Excelência em Eficiência Energética da Amazônia da UFPA. O projeto, o primeiro na área de Mobilidade Elétrica no Estado do Pará, denominado de “Sistema Inteligente Multimodal da Amazônia”, implantado no campus Universitário do Guamá e de Castanhal, serve como um laboratório vivo, para análise e desenvolvimento de tecnologias e modelo de negócios, atendendo a toda comunidade universitária e ribeirinhos. Este trabalho apresenta alguns dos subsistemas e serviços ofertados para a implantação de um sistema de Mobilidade Elétrica na região e, uma cartilha informativa/educativa para os usuários dos modais.

Among the main problems faced in the context of electric mobility today, the management and monitoring of electric vehicle charging stations, the integration between the diverse types of technologies that make up its architecture, and its low scalability stand out. Therefore, we will present the implementation and complete integration of an electric vehicle charging system in an electric mobility pilot project being executed in the Amazon region in Brazil. Therefore, a literature review of related works will be presented, and its entire implementation will be addressed, from the charging infrastructure, through its back-end system and its Internet of things platform, to its front-end web system for monitoring charging stations. In addition, a complete prototype is created with a real testbed to verify the scalability of the implemented physical system. Based on the testbed evaluations performed, we observe that the implemented system performs well in receiving and sending data from up to 160 electric vehicle charging stations, achieving an average consumption of 26% for CPU and 95% for memory. In addition, it is important to mention that the deployed system supports horizontal scalability, enabling the connection of more charging stations and making it ideal for other integrated systems similar to ours. Based upon the main results obtained with the implemented system, the possibility of carrying out the management and monitoring of charging stations stands out; the integration of different technologies, from the back end and IoT middleware to its front end; a system that supports scalability, enabling the connection of more charging stations; and a reference architecture for charging station management and monitoring systems for the Amazon region.

The Internet of Things (IoT) device scenario has several emerging technologies. Among them, Low-Power Wide-Area Networks (LPWANs) have proven to be efficient connections for smart devices. These devices communicate through gateways that exchange points with the central server. This study proposes an empirical and statistical methodology based on measurements carried out in a typical scenario of Amazonian cities composed of forests and buildings on the Campus of the Federal University of Pará (UFPA) to apply an adjustment to the coefficients in the UFPA propagation model. Furthermore, an Evolutionary Particle Swarm Optimization (EPSO) metaheuristic with multi-objective optimization was applied to maximize the coverage area and minimize the number of gateways to assist in the planning of a LoRa network. The results of simulations using the Monte Carlo method show that the EPSO-based gateway placement optimization methodology can be used to plan future LPWAN networks. As reception sensitivity is a decisive factor in the coverage area, with −108 dBm, the optimal solution determined the use of three gateways to cover the smart campus area.

Photovoltaic systems can strengthen the energy matrix aiming at energy sustainability, however, their intermittence, the availability time frame and seasonality effects are complicating aspects for the operation of these systems. To mitigate such problems, a possible measure is the operation of hybrid systems (HS) associated with storage systems to meet the energy demand. This study presents real operational scenarios for the management of a hybrid mini-grid installed in the Amazon region in Brazil, capable of managing energy supply and demand, changing the generation curve, reducing fossil fuel consumption and energy costs, in addition to providing flexibility and operational safety to the electrical operating under on-grid or off-grid conditions. Through technical and economic analyses, the objective of this study is to show that hybrid systems can be implemented in small and medium consumers, in addition to generating a new market option for utilities. This research evaluates the operation of HS concurrently with the electrical grid and the results show that the system meets the technical requirements, in addition to bringing financial and ecological benefits. This information can support the decisions of consumers, utilities and energy operators for the development of the national market for on-grid and off-grid HS.