Abstract
Currently, energy consumption in residential buildings is increasingly high. To meet demand, renewable energies are increasingly being used to produce more energy in a sustainable way, which has led to an increase in the load on the distribution network. Thus, with the exponential growth of dependence on technologies, studies on consumption patterns are increasingly common in order to try to understand the needs of the population and, in this way, make a more rational and efficient use of energy. This article aims to find consumption patterns in residential devices, considering specific houses. This work proposes the use of the Apriori algorithm, which allows the creation of several association rules among devices. The results, considering several scenarios in a house with 9 appliances, show that, despite the Apriori algorithm's difficulty in finding associations in household appliances with little time of use, several interesting association rules can be identified, providing relevant insights for future consumption flexibility models applications.

Abstract
Deep learning networks, more specifically convolutional neural networks, have shown a notable distinction when it comes to computer vision problems. Their versatility spans various domains, where they are applied for tasks such as classification and regression, contingent primarily on the availability of a representative dataset. This work explores the feasibility of employing this approach in the domain of agriculture, particularly within the context of olive growing. The objective is to enhance and facilitate cultivar identification techniques by using images of olive tree leaves. To achieve this, a comparative analysis involving ten distinct convolutional networks (VGG16, VGG19, ResNet50, ResNet152-V2, Inception V3, Inception ResNetV2, XCeption, MobileNet, MobileNetV2, EfficientNetB7) was conducted, all initiated with transfer learning as a common starting point. Also, the impact of adjusting network hyperparameters and structural elements was explored. For the training and evaluation of the networks, a dedicated dataset was created and made available, consisting of approximately 4200 images from the four most representative categories of the region. The findings of this study provide compelling evidence that the majority of the examined methods offer a robust foundation for cultivar identification, ensuring a high level of accuracy. Notably, the first nine methods consistently attain accuracy rates surpassing 95%, with the top three methods achieving an impressive 98% accuracy (ResNet50, EfficientNetB7). In practical terms, out of approximately 2016 images, 1976 were accurately classified. These results signify a substantial advancement in olive cultivar identification through computer vision techniques.

Abstract
[No abstract available]

Abstract
Nowadays, human operators at control centers analyze a large volume of alarm information during outage events and must act fast to restore the service. To assist operator decisions this work proposes novel machine learning-based functions aiming to: (a) classify the complexity of a fault occurrence (Occurrences Classifier) and its cause (Fault Cause Classifier) based on its alarm events; (b) provide fast insights to the operator on how to solve it (Data2Actions). The Occurrences Classifier takes alarm information of an occurrence and classifies it as a simpleor complexoccurrence, while the Fault Cause Classifier predicts the cause class of MV lines faults. The Data2Actions takes a sequence of alarm information from the occurrence and suggests a more adequate sequence of switching actions to isolate the fault section. These algorithms were tested on real data from a Distribution System Operator and showed: (a) an accuracy of 86% for the Data2Actions, (b) an accuracy of 68% for the Occurrences Classifier, and (c) an accuracy of 74% for the Fault Cause Classifier. It also proposes a new representation for SCADA event log data using graphs, which can help human operators identify infrequent alarm events or create new features to improve model performance.

Abstract
We spend about one-third of our life either sleeping or attempting to do so. Sleeping is a key aspect for most human body processes, affecting physical and mental health and the ability to fight diseases, develop immunity and control metabolism. Therefore, monitoring human sleep quality is extremely important for the detection of possible sleep disorders. Several technologies exist to achieve this goal, however, most of them are expensive proprietary systems, some require hospitalization and many use intrusive equipment that can, by itself, affect sleep quality. This paper presents an intelligent system, a complete low-cost hardware and software solution, for monitoring the sleep quality of an individual in a home environment. User privacy is guaranteed as all processing is done at the edge and no audio or video is stored. This system monitors several fundamental aspects of sleeping periods in real-time using a low cost single-board computer for processing, a camera for body motion detection (MD module) and for eye/sleep status detection (SSD module), and a microphone for audio recognition (AUDR module) of breath pattern analysis and snore detection. It can be strategically placed near the bed to avoid interfering with the natural sleep pattern. For each sleeping period, the system produces a final report that can be a valuable aid for improving the sleeping health of the monitored person. Functional unitary tests were carried successfully on the selected, low-cost, hardware platform (Raspberry Pi). The entire process was validated by an expert clinical psychologist, ensuring the reliability and effectiveness of the system. The visual and sound modules use sophisticated computer vision and machine learning techniques suitable for edge computing devices. Each of the system’s features have been independently tested, using properly organized audio and video datasets and the well established metrics of precision, recall and F1 score, to evaluate the binary classifiers in each of the three modules. The accuracy values obtained where 90.2% (MD), 79.1% (SSD) and 81.3% (AUDR), demonstrating the great application potential of our solution.

Abstract