This study explores the application of Failure Modes and Effects Analysis (FMEA) to ground-mounted and Floating Photovoltaic (FPV) systems, with and without tracking systems, considering the particularities of each configuration. The FMEA methodology was applied to identify and prioritize failure modes, analyzing severity, occurrence and detection, as well as calculating the Risk Priority Number (RPN) and the Action Priority (AP). The FMEA methodology was adopted due to its preventive capability in identifying early-stage component failures and prioritizing actions. To this end, detailed analyses of the main failure modes and effects were carried out, covering critical components such as photovoltaic modules, inverters and support systems. In addition, preventive and corrective actions will be proposed. The study includes the case study of the Research Network for Technological Development and Innovative Extension of Advanced Materials in Energy and Mobility applied to the Amazon context (EMOBAMAZON). This study represents a novel application of FMEA to Floating Photovoltaic (FPV) systems in tropical environments, demonstrating its potential to improve predictive maintenance and operational reliability in renewable energy facilities. The integration of risk classification approaches has enabled a comprehensive and well-founded view of failures, contributing to the reliability, safety and operational efficiency of photovoltaic systems in the Amazon context.

ELEN PRISCILA LOBATO, EDUARDO MONTEIRO, YASMIM LISBOA, JOÃO ARAÚJO, WELLINGTON FONSECA

The global transition toward sustainable mobility and renewable energy integration demands intelligent energy management frameworks capable of coupling electric mobility, distributed generation, and energy storage. This study presents a comprehensive evaluation of the SIMA Project (Sistema Inteligente Multimodal da Amazônia), an innovative mobility pilot implemented at the Federal University of Pará, Brazil. The SIMA consists of the monitoring building, photovoltaic systems, lithium-based energy storage systems, and electric transportation modes (including urban and intercity buses, as well as a solar-powered catamaran), all interconnected within a microgrid. Field monitoring, data processing, and simulation analyses were conducted to assess energy performance, consumption patterns, and the operational feasibility of these electric systems under Amazonian conditions. The results indicate that the PV systems supply most of the SIMA’s demand, with the laboratory building accounting for 70% of total consumption and electric vehicles for 30%. Simulated full operation scenarios reveal the potential for near net-zero energy balance when energy management strategies are applied to generation, storage and charging. The findings demonstrate the technical viability of integrated mobility–energy systems in tropical contexts and provide practical insights for future low-carbon transport infrastructures in isolated or city-scale networks.

BRUNO SANTANA DE ALBUQUERQUE, AYRTON LUCAS LISBOA DO NASCIMENTO, MARIA EMÍLIA DE LIMA TOSTES, UBIRATAN HOLANDA BEZERRA, CARMINDA CÉLIA MOURA DE MOURA CARVALHO, JONATHAN MUÑOZ TABORA

This study focuses on the mathematical modeling, control design, and analysis of an interleaved bidirectional high-voltage-gain DC-DC converter for energy management in supercapacitors. The state of the art is reviewed, with an emphasis on research related to DC-DC converters and energy storage systems. The characteristics and modeling of the supercapacitors are thoroughly analyzed. The converter’s operation in both buck and boost modes is described, detailing its operating stages, design parameters, and component sizing. The modeling accounts for the dynamics of the converter in both operational modes. PI controllers and compensation techniques were implemented to ensure the desired performance and meet the design criteria. Simulations were conducted using PSIM software, version 2023.1, with a power flow of 1 kW, a 48 V DC bus (buck mode), and a 162 V supercapacitor module (boost mode), operating at 500 kHz. The performance of the controllers was evaluated during both the charging and discharging processes of the supercapacitor, analyzing the dynamic response and behavior in the continuous mode, even in the presence of system disturbances.

JESSICA C. A. SOUSA, THIAGO M. SOARES, JONATHAN M. TABORA, HUGO G. LOTT

This study proposes a simplified mathematical formulation for optimizing isolated microgrids, enhancing computational efficiency while preserving solution quality. The research focuses on the influence of Operation and Maintenance (O&M) costs for Non-Dispatchable Generators (NDGs) and the relationship between costs and pollutant emissions. The proposed simplification reduces computational requirements, improves result interpretability, and increases the scalability of optimization techniques. The O&M costs of photovoltaic and wind systems were excluded from the initial optimization and calculated afterward. A Student’s t-test yielded a p-value of 87.3%, confirming no significant difference between the tested scenarios, ensuring that the simplification does not impact solution quality while reducing computational complexity. For emission-related costs, scenarios with single and multiple pollutant generators were analyzed. When only one generator type is present, modifications are needed to enable effective multi-objective optimization. To address this, two alternative mathematical formulations were tested, offering more suitable approaches for the problem. However, when multiple pollutant sources exist, cost and emission differences naturally define the problem as multi-objective without requiring adjustments. Future work will explore grid-connected microgrids and additional optimization objectives, such as loss minimization, voltage control, and device lifespan extension.

VITOR DOS SANTOS BATISTA, THIAGO MOTA SOARES, MARIA EMÍLIA DE LIMA TOSTES, UBIRATAN HOLANDA BEZERRA, HUGO GONÇALVES LOTT

Photovoltaic systems are among the renewable energy sources with the greatest global impact, driven by technologies that enable real-time monitoring, predictive maintenance, and intelligent integration with the electricity grid. In this context, this paper presents the design and implementation of an embedded Internet of Things (IoT) system to monitor temperature and humidity in photovoltaic systems in the Amazon region. The system was implemented in a photovoltaic solar plant located at the Federal University of Pará and used to monitor parameters such as local humidity and temperature, with the latter being considered at three strategic points: the surface of the photovoltaic module exposed to direct solar radiation, the shaded area of the module, and the ambient temperature. The results obtained showed good performance from the embedded system, with emphasis on the ease of remotely updating the embedded system’s code and centralized visualization of the monitored data in an IoT middleware. The device proved to be resistant to the adverse climatic conditions of the Amazon, allowing the operators and managers of the photovoltaic plant to monitor and visualize the measured variables and to draw up preventive and corrective maintenance strategies. In this way, the embedded system designed and implemented is a valuable tool for the photovoltaic plant’s operators and managers, promoting greater energy efficiency, reducing operating costs and increasing the useful life of the modules. It also contributes to the Sustainable Development Goals (SDGs), such as SDG 7 (Clean and affordable energy) and SDG 13 (Climate action).

YASMIM LISBOA, LUCAS SANTOS, ELEN LOBATO, WELLINGTON FONSECA, KAYLANE SILVA, IRIS RODRIGUES, MARCELO SILVA

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.

JOSIVAN RODRIGUES DOS REIS, JONATHAN MUÑOZ TABORA, MATHEUS CARVALHO DE LIMA, FLÁVIA PESSOA MONTEIRO, SUZANE CRUZ DE AQUINO MONTEIRO, UBIRATAN HOLANDA BEZERRA

Electric mobility is a global trend and necessity, with electric and solar boats offering a promising alternative for transportation electrification and carbon emission reduction, especially in the Amazon region. This study analyzes the system of a solar boat from an electric mobility project—to be implemented at Universidade Federal do Pará (UFPA)—using MATLAB software for modeling. The Simulink tool was utilized to model the system, focusing on operational parameters such as module voltage, converter voltage, and speed. The results indicate that the solar boat’s operational cost is significantly lower compared to a similar internal combustion model, considering diesel’s high consumption and cost. The environmental impact is also reduced, with nearly 72 tons of CO2 emissions avoided annually, thanks to Brazil’s renewable energy matrix. Simulations confirmed the project’s parameters, demonstrating the efficiency of digital-twin technology in monitoring and predicting system performance. The study underscores the importance of digital twins and renewable energy in promoting sustainable transportation solutions, advocating for the replication of such projects globally. Future research should focus on further advancing digital-twin applications in electric mobility to enhance predictive maintenance and operational efficiency.

BRUNO SANTANA DE ALBUQUERQUE, MARIA EMÍLIA DE LIMA TOSTES, UBIRATAN HOLANDA BEZERRA, CARMINDA CÉLIA MOURA DE MOURA CARVALHO, AYRTON LUCAS LISBOA DO NASCIMENTO

Artificial intelligence algorithms play a key role in solving multimodal urban mobility problems, this study outlines a scheme covering bus, boat, and pedestrian transport modes. This involves designing an Artificial Neural Network (ANN) model to classify the most suitable routes for passengers, together with an algorithm capable of estimating the arrival times of various transportation modes at designated stopping points or buildings within the smart campus. The information used to train the ANN is obtained from an Internet of Things (IoT) network and a database that includes the available routes within the campus (which is situated in the Brazilian Amazon), where there is a multimodal electric mobility service. In seeking to achieve the objectives, this ANN relied on the user's geographic location in the input layer, and route mapping data stored in the database in the output layer, as well as the backpropagation algorithm for computational processing and adjustments of synaptic weight. The algorithm for modal arrival time prediction used real-time geographic location data, together with the Haversine formula to calculate geographic distance, while the average speed was obtained from the IoT network. A range of different ANN parameters were included in the experiments. In the study case, the ANN results show an improvement of above 92% for determining the best routes and predicting arrival times. The findings show that ANNs can effectively find the best route within a real-world smart campus environment. Moreover, the results show the arrival time can be estimated by using real-time geographic location data.

JOINER DOS SANTOS SÁ, EDINHO DO NASCIMENTO DA SILVA, LEONARDO NUNES GONÇALVES, CAIO MATEUS MACHADO CARDOSO, ANDRÉIA ANTLOGA DO NASCIMENTO, GERVÁSIO PROTÁSIO DOS SANTOS CAVALCANTE, MARIA EMÍLIA DE LIMA TOSTES, JASMINE PRISCYLA LEITE DE ARAÚJO, FABRÍCIO JOSE BRITO BARROS, FABRICIO DE SOUZA FARIAS

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.

ANA CAROLINA DIAS BARRETO DE SOUZA, FILIPE MENEZES DE VASCONCELOS, JACKQUELLINE C. DO N. AZEVEDO, LARISSA PAREDES MUSE, GABRIEL ABEL MASSUNANGA MOREIRA, JOÃO VICTOR DOS. REIS ALVES, MARIA EMÍLIA DE LIMA TOSTES, CARMINDA CÉLIA MOURA DE MOURA CARVALHO, ANDRÉIA ANTLOGA DO NASCIMENTO

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.

ANA CAROLINA DIAS BARRETO DE SOUZA, FILIPE MENEZES DE VASCONCELOS, GABRIEL ABEL MASSUNANGA MOREIRA, JOÃO VICTOR DOS REIS. ALVES, JONATHAN MUÑOZ TABORA, MARIA EMÍLIA DE LIMA TOSTES, CARMINDA CÉLIA MOURA DE MOURA CARVALHO, ANDRÉIA ANTLOGA DO NASCIMENTO