This work investigates the efficiency of the process of load disaggregation, considering only the values of active power. To perform the task, we use data collected from the NILM (Non-Intrusive Load Monitoring) measurement method, presented in the Rainforest Automation Energy Dataset (RAE) and Reference Energy Disagreggation Dataset (REDD) database. A strategy of assigning labels using combinations of equipment in use, by status ON/OFF, and also by choosing an appropriate temporal data window is discussed. Also, the performance of very well-known machine learning algorithms such as k-Nearest Neighbor (kNN), Decision Tree, and Random Forest are evaluated. The results show a very efficient and low computer complexity strategy presenting values of F1-Score above 95%, for RAE and REDD database. As presented in table I, the proposed approach presents the highest F1-Score, compared to other methods in the literature, considering all appliances in the REDD database. The greatest benefit of the approach consists in the possibility of applying the disaggregation process in a household without smart outlets, under the restriction that the training and test houses hold identical or similar appliances.

This paper proposes and implements a smart architecture for Home Energy Management Systems (HEMS) that enables interoperability among devices from different manufacturers. This is achieved through the use of standardized elements and the design of an innovative middleware. The system comprises a control unit that communicates with smart outlets using the Wireless Smart Ubiquitous Network (WI-SUN) Home Area Network (HAN) specification, while smart metering is achieved using the WI-SUN Field Area Network (FAN) specification. To manage important data, a web platform and mobile app were created. Additionally, machine learning techniques are utilized to identify energy consumption of individual appliances when only the aggregate energy consumption of the house is available. The architecture presented here supports real-time control of energy use and generation through HEMS, and new devices can be added transparently. Finally, a comparison of the proposed system with similar systems in literature highlights its many advantages in terms of functionality.

A middleware software can be seem as an abstraction layer between hardware and user applications, that facilitates the development and deployment of services in various scenarios, such as those found in Home Energy Management Systems (HEMS). There are several middleware proposals for HEMS, with most of them taking the cloud computing approach. This approach is unconcerned about computing resources but raises a dependency on external connections. This paper presents a middleware for energy management systems, based on the concept of edge computing for smart homes. The paper presents a reference model for the proposed architecture, considering specific requirements for this type of application. The proposed architecture employs the concept of microservices for data access and system configuration. The proposed middleware is designed to work with embedded systems under computational constraints, such as processing capability and storage, to reduce costs and allow its application closer to the user. The middleware is open and customizable to meet the developer’s needs. The proposed solution was implemented and tested in a university laboratory, as well as at the Eldorado Research Institute to confirm the effectiveness of the middleware. The proposal stands out from others found in the literature as it can be implemented using low cost hardware. In addition to using microservices concepts, the proposed middleware is a valuable option for applications that need an edge computing approach. A performance analysis was carried out, using low cost hardware with limited resources. The results show that the proposal can handle a significant number of devices, offering low latency and low error rate, and consuming few processing resources and memory.

The increasing domestic demand for electricity has required the scientific community to develop new technologies focused on energy expenditure minimization. In this context, Home Energy Management System (HEMS) is an element that can incorporate applications that aid in efficient energy management, such as load recognition. Load recognition allows HEMS to map all appliances, assisting in efficient management. In this regard, methods that solve the load recognition problem are vital for HEMS. The motivation of this work is to propose a novel system that enhances load recognition in HEMS applications. For this purpose, the proposed system contains an event detector stage based on the Wavelet transform and another stage for appliance identification consisting of two processing chains, one for high reliability and one for fast model training. The main novelties of our work are: (i) very-low training time via fast training processing chain, less than 1 second with k -Nearest Neighbor (k-NN); (ii) the highest values for accuracy, F ₁ , and the kappa index, 98.90%, 98.77%, and 0.9382, respectively, for the regularized Convolutional Neural Network (CNN-r) using the ‘Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology’ (REFIT) dataset; (iii) this is the first paper employing CNN-r and Vision Transformer (ViT) for load recognition problem; and (iv) the approach is applicable in households without any restrictions, as long as training the models. Regarding the tested scenarios, the results reveal a very efficient strategy yielding accuracy values greater than 98% for the REFIT dataset and the Reference Energy Disaggregation Dataset (REDD). Finally, as presented in Table 1, the proposed approach provides the highest performance compared to other methods available in the literature.

This work investigates the efficiency of a fault detection system for refrigeration equipment based on Wavelet Transform. To perform the task, we used the well–known database REFIT (Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology). We used data from six devices presented in three households of the database considering readings of active power as features. The results revealed an approach with high efficiency in detecting abnormal behavior of the tested appliances and presented low runtime despite large volumes of input data. Another positive point to highlight, in the proposed method, is the capacity to estimate the instants at which faults start and cease to occur. Considering the viewpoint of consumers, obtaining knowledge about the malfunctioning of household appliances enables timely repairs, resulting in financial savings and improved overall quality of life. Moreover, by setting parameters according to the characteristics of the appliances, the proposed method is suitable for different equipment, thereby establishing feasible fault detection for diverse groups of devices.

Mapping areas with water bodies is crucial for monitoring water systems and resource management, as well as for guiding urban expansion and land-use planning, thus contributing to sustainable resource management, flood prevention, and the conservation of aquatic ecosystems. While satellite images offer broad territorial coverage, drones provide high spatial resolution of the Earth's surface. In this research, the Pulse Coupled Neural Network (PCNN) [1] image fusion method and the GDAL Pansharpening method, the latter being available in QGIS, were compared to determine which is more effective in merging images to highlight water surface areas in a case study, combining the spectral coverage of satellites with the precision of drones. The implemented PCNN model was able to overcome QGIS Pansharpening method, which is based on Brovey algorithm, obtaining better metrics. This study of drone and satellite image fusion has shown to be a promising approach to overcome the limitations of these technologies when used individually.