Digital integration is essential for efficient and sustainable management of energy resources, especially in modernizing outdated infrastructures. This involves embedding sensor and actuator devices for precise energy monitoring and control, supported by distributed computing from edge to cloud, necessary for tailored analytical and predictive decisions. Retrofit strategies incorporate these resources into older systems, enhancing existing infrastructures while adapting to technological progress. Nevertheless, systematic methodologies for sensor-actuator integration are lacking in the literature. Therefore, this article introduces the SmartLVEnergy framework, designed to modernize legacy low-voltage systems with a retrofitting approach, integrating decentralized sensing, control, distributed processing, and predictive analytics. The framework was employed to retrofit the energy distribution panel of a legacy manufacturing plant with sensor devices, which support remote monitoring and decentralized predictive analysis using 15-minute energy demand forecast models based on 2-layer Long Short-Term Memory (LSTM) networks, adhering to Artificial Intelligence of Things (AIoT) and Tiny Machine Learning (TinyML) principles. Benchmarked against non-quantized and single-layer LSTM models for the installation energy demand forecast between May 8 and July 11, 2023, our approach showed reduced prediction latency and memory consumption. The main circuit breaker exhibited a 96.67% R-squared score (R ² ) and 7.23 kW Root Mean Squared Error (RMSE), with the associated sensor showing a prediction latency of 54.55 ms and using 58.45 KB of FLASH memory. These metrics for the best-performing sensor were 97.33%, 0.39 kW, 5.55 ms, and 10.20 KB. This AIoT framework marks a significant advancement in sensor-actuator retrofitting, blending digital technology with legacy systems for intelligent energy management.

The Amazon Rainforest represents one-third of the world’s tropical forest area. This makes it indispensable for maintaining global biodiversity. However, the increasing occurrences of wildfires and deforestation in the region are notorious. In this sense, it is essential to protect forests to ensure quality of life for future generations and prevent damage that affects the entire planet. In this work, a real-time monitoring device is proposed to identify attempts at deforestation through audio signals from tractors and chainsaws, using embedded artificial intelligence (AI). Additionally, it is capable of communicating with a base station, reaching distances close to 1 km in dense forest, through long-range (LoRa) communication. A user interface is also developed, providing daily alerts such as attack identification, occurrence times, device locations, and battery status. The system has an average power consumption of around 300 nA, employing power management methods defined as ultra-low power mode, sleep mode, prediction mode, and transmission mode. Hence, the device has the potential to promote the sustainable preservation of the Amazon Rainforest, helping to prevent large-scale illegal deforestation.

Energy demand forecasting is crucial for planning and optimizing the use of energy resources in building facilities. However, integrating digital solutions and learning techniques into legacy buildings presents significant challenges due to limited or outdated resources, hampering predictive analytics in these buildings and their circuits. To fill this gap, this article proposes an innovative demand forecasting strategy using an AIoT retrofit architecture based on the SmartLVGrid metamodel. This architecture allows remote monitoring of legacy building circuits, facilitating the collection, processing and storage of data in the cloud. We use several learning algorithms, including linear regression, support vector regressor, random forest regressor, XGBoost regressor, and long short-term memory (LSTM) neural network, to predict energy demand 15 min ahead, identifying potential overruns of contracted demand in accordance with Brazilian regulations. After Bayesian optimization, the LSTM neural network outperformed other models for most of the selected datasets and detected 32 out of 38 demand overruns on the test set. XGBoost and random forest followed closely, detecting 30 demand overruns. Overall, our cost-effective solution optimizes energy usage and efficiently mitigates potential demand exceedances in building installations. This is achieved through a step-by-step approach to upgrading existing aging facilities, which promotes energy efficiency and sustainability.

Predicting energy demand in adverse scenarios, such as the COVID-19 pandemic, is critical to ensure the supply of electricity and the operation of essential services in metropolitan regions. In this paper, we propose a deep learning model to predict the demand for the next day using the “IEEE DataPort Competition Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm” database. The best model uses hybrid deep neural network architecture (convolutional network–recurrent network) to extract spatial-temporal features from the input data. A preliminary analysis of the input data was performed, excluding anomalous variables. A sliding window was applied for importing the data into the network input. The input data was normalized, using a higher weight for the demand variable. The proposed model’s performance was better than the models that stood out in the competition, with a mean absolute error of 2361.84 kW. The high similarity between the actual demand curve and the predicted demand curve evidences the efficiency of the application of deep networks compared with the classical methods applied by other authors. In the pandemic scenario, the applied technique proved to be the best strategy to predict demand for the next day.

Nos dias atuais, o uso de caixas eletrônicos tornou -se uma prática mundial, e muito comum encontrar estes caixas em diversas locações sob diferentes condições tanto climáticas quanto de operação. Dentro dos esforços na busca de soluções, especial atenção e brindada a testar diferentes propriedades tanto destas maquinas quanto das suas partes. Neste trabalho são apresentados resultados da aplicação do software Inventor no desenvolvimento de um projeto para a fabricação de um dispositivo de testes para validação da pressão individual de cada tecla do dome sheet que compõe o teclado aplicado a dispositivos de caixas eletrônicos. O sistema uma vez construído deverá ser capaz de realizar a verificação da força necessária para acionamento de cada uma das teclas no mencionado dome sheet formado por duas classes de teclas: as teclas numéricas e as teclas de função cujas especificações de pressão de acionamento são diferentes e precisam ser validadas. O principal resultado do trabalho foi justamente a obtenção tanto dos desenhos 3D quanto dos layouts ou detalhamentos 2D de todas as partes componentes do equipo que possibilitou de forma exitosa sua fabricação e o desenvolvimento dos testes demandados

A floresta amazônica corresponde a um terço da área de florestas tropicais do mundo. Isso a torna imprescindível na manutenção da biodiversidade global. No entanto, é notório o crescimento de ocorrências de queimadas e desmatamento na região. O desmatamento causa danos a todo o planeta, e proteger as florestas é essencial para a garantia de qualidade de vida das próximas gerações. Neste trabalho são apresentados resultados da aplicação do software Inventor no desenvolvimento de um projeto de um dispositivo capaz de monitorar regiões remotas como a floresta amazônica e áreas de preservação. Por exemplo, detectar a extração de madeira na floresta amazônica pode ser um desafio devido sua extensão florestal e às dificuldades de acesso a algumas áreas. O sistema uma vez construído deverá ser capaz de identificar barulho de motosserras e tratores através de inteligência artificial e enviar um alerta utilizando comunicação LoRaWan. No presente trabalho é apresentado o desenvolvimento do projeto mecânico deste dispositivo; os resultados obtidos radicam justamente na obtenção tanto dos desenhos 3D quanto dos layouts ou detalhamentos 2D de todas as partes componentes do equipo que permitiram construir e testar o mesmo com resultados positivos nos diferentes testes demandados.