Great concern regarding energy efficiency has led the research community to develop approaches which enhance the energy awareness by means of insightful representations. An example of intuitive energy representation is the parts-based representation provided by Non-Intrusive Load Monitoring (NILM) techniques which decompose non-measured individual loads from a single total measurement of the installation, resulting in more detailed information about how the energy is spent along the electrical system. Although there are previous works that have achieved important results on NILM, the majority of the NILM systems were only validated in residential buildings, leaving a niche for the study of energy disaggregation in non-residential buildings, which present a specific behavior. In this paper, we suggest a novel fully-convolutional denoising auto-encoder architecture (FCN-dAE) as a convenient NILM system for large non-residential buildings, and it is compared, in terms of particular aspects of large buildings, to previous denoising auto-encoder approaches (dAE) using real electrical consumption from a hospital facility. Furthermore, by means of three use cases, we show that our approach provides extra helpful funcionalities for energy management tasks in large buildings, such as meter replacement, gap filling or novelty detection.

The industry is currently undergoing a digital revolution driven by the integration of several enabling technologies. These include automation, robotics, cloud computing, industrial cybersecurity, systems integration, digital twins, etc. Of particular note is the increasing use of digital twins, which offer significant added value by providing realistic and fully functional process simulations. This paper proposes an approach for developing digital twins in industrial environments. The novelty lies in not only focusing on obtaining the model of the industrial system and integrating virtual reality and/or augmented reality but also in emphasizing the importance of incorporating other enabled technologies of Industry 4.0, such as system integration, connectivity with standard and specific industrial protocols, cloud services, or new industrial automation systems, to enhance the capabilities of the digital twin. Furthermore, a proposal of the software tools that can be used to achieve this incorporation is made. Unity is chosen as the real-time 3D development tool for its cross-platform capability and streamlined industrial system modeling. The integration of augmented reality is facilitated by the Vuforia SDK. Node-RED is selected as the system integration option, and communications are carried out with MQTT protocol. Finally, cloud-based services are recommended for effective data storage and processing. Furthermore, this approach has been used to develop a digital twin of a robotic electro-pneumatic cell.

A new industrial production model based on digitalization, system interconnection, virtualization and data exploitation, has emerged. Upgrade of production processes towards this Industry 4.0 model is one of the critical challenges for the industrial sector and, consequently, the training of students and professionals has to address these new demands. To carry out this task, it is essential to develop educational tools that allow students to interact with real equipment that implements, in an integrated way, new enabling technologies, such as connectivity with standard protocols, storage and data processing in the cloud, machine learning, digital twins and industrial cybersecurity measures. For that reason, in this work, we present an educational environment on Industry 4.0 that incorporates these technologies reproducing realistic industrial conditions. This environment includes cutting-edge industrial control system technologies, such as an industrial firewall and a virtual private network (VPN) to strengthen cybersecurity, an Industrial Internet of Things (IIoT) gateway to transfer process information to the cloud, where it can be stored and analyzed, and a digital twin that virtually reproduces the system. A set of hands-on tasks for an introductory automation course have been proposed, so that students acquire a practical understanding of the enabling technologies of Industry 4.0 and of its function in a real automation. This course has been taught in a master’s degree and students have assessed its usefulness by means of an anonymous survey. The results of the educational experience have been useful both from the students’ and faculty’s viewpoint.

This paper presents a web-based software tool for tutoring support of engineering students without any need of data scientist background for usage. This tool is focused on the analysis of students’ performance, in terms of the observable scores and of the completion of their studies. For that purpose, it uses a data set that only contains features typically gathered by university administrations about the students, degrees and subjects. The web-based tool provides access to results from different analyses. Clustering and visualization in a low-dimensional representation of students’ data help an analyst to discover patterns. The coordinated visualization of aggregated students’ performance into histograms, which are automatically updated subject to custom filters set interactively by an analyst, can be used to facilitate the validation of hypotheses about a set of students. Classification of students already graduated over three performance levels using exploratory variables and early performance information is used to understand the degree of course-dependency of students’ behavior at different degrees. The analysis of the impact of the student’s explanatory variables and early performance in the graduation probability can lead to a better understanding of the causes of dropout. Preliminary experiments on data of the engineering students from the 6 institutions associated to this project were used to define the final implementation of the web-based tool. Preliminary results for classification and drop-out were acceptable since accuracies were higher than 90% in some cases. The usefulness of the tool is discussed with respect to the stated goals, showing its potential for the support of early profiling of students. Real data from engineering degrees of EU Higher Education institutions show the potential of the tool for managing high education and validate its applicability on real scenarios.