Project Listing

Energy applications of electrochemical technology High performance organic cathode materials for lithium-ion rechargeable batteries have received significant research interest because sustainability, flexibility and versatile molecular design. Many organic cathode materials such as active quinone-polymers have demonstrated optimal reversible lithium insertion corresponding to partial reduction of quinone groups and a capacity of up to 150mAhg-1. Based […]

The study of energy consumption habits in consumers is useful to save money, energy and decrease pollution. Nowadays, the big amount of data managed in market statistics requires a certain grade of automation to process the information within an acceptable period of time.
In this regard, automated recognition in images can greatly help to perform energy market studies where visual inspection is required. The application of camera-based systems has exponentially grown in recent years due to the improvements in camera features and computer vision techniques. Besides, the rise of deep learning in the last decade has greatly enhanced the effectiveness of computer vision systems, which can automatically compute and extract very relevant information from images.
According to the previous considerations, the goal of the proposed project is to research on innovative computer vision and deep learning algorithms with the aim of contributing in the automation of energy consumption market studies based on information obtained from images.

Smart Grids (SG) are a result of the progressive application of information and telecommunication technologies to traditional electrical grids. SG services enable the introduction of grid edge technologies, resulting in a changing energy production and delivery model, with greater control and involvement by customers. SG services are both a desire and a must towards grid sustainability and efficiency.

The goal of the proposed research line is to develop an automated parking detection system based on computer vision techniques distributed in a mobile application across several users, together with a centralized server that maps the city parking slots and their availability by using user’s data. The server would construct a complete map view of the street parking slots in the city by making use of the processed camera information from the user’s phones, which is geolocated to the user’s location by using GPS and visual odometry. The server would continuously feed the built map and information about available spots to the users, so they can take advantage of this data to find a parking slot much faster. Such a system could significantly lower the time that drivers spend looking for parking slots, directly reducing the associated costs of parking and congestion in cities.

Sustainable development requires accounting for social and environmental costs of policy decisions. Energy consumption is inextricably linked to different environmental processes such as climate change, water regulation and biodiversity impact and hence to human well being. The aim of this research line is to develop and parameterize socio-ecosystem models to identify tradeoffs in ecosystem services linked to energy use to generate guidelines for sustainable use. The focus in highly interdisciplinary; including ecology, social sciences, computer science and engineering to develop an integrated view of the system.

Intelligent vehicles technologies require a good understanding of human driver behaviors to guarantee safe, adjust to drivers’ needs and meet their preferences. This research line try to answer these questions monitoring driving behaviors by using the emerging deep learning approach, based on big data, which is revolutionizing the classical machine learning techniques, getting a breakthrough in the performance of complex classification and decision-making problems. Our proposal is to monitor driver behavior in real-time and to carry out decision making tasks, by using deep learning techniques, in order to implement a safe switching system between manual and automatic mode in the future V2U (Vehicle to User) interfaces for autonomous vehicles.

The Environmental Humanities, and specifically ecocriticism, aesthetics and philosophy, focus on cultural attitudes that condition and affect the reactions and perceptions of the local population to environmental issues and changes through the study of literature, the arts and other cultural expressions. Science, industry and the media have not been very effective in communicating the environmental crisis and the impending structural, social, cultural and paradigmatic changes that climate change will force upon our civilization and lifestyle, including, among others, the structure of cities, transport, infrastructure, food, leisure and our aesthetic perception of landscapes and the use of private and public spaces.

EPC is one of the most relevant tool for implementing energy efficiency measures in buildings. The research activity will be focused on the development of novel EPC model, considering smart contracts based on blockchain technology. The EPC definition will also consider associated business models and related side disciplines (i.e. social science and humanities).

Power network analysis have been mostly based on HV and MV signals, while today availability of smart meters data in (near) real-time is disclosing new possibilities for optimal control. Moreover, the increasing number of dispersed generators is focusing the objective on low voltage grid monitoring and control.

This research activity will be focused on integrate the smart metering signals and data from different utility metering infrastructures, into a unified platform able to ingest data with different models and protocols (DLMS/COSEM, PLC Prime, M-Bus, etc.). The expected result is the capacity to acquire and expose data from different utilities (i.e. energy carriers) into a unique centralized solution.