Project Listing

Energy demand prediction is a relevant problem whose solution is evaluated by policy makers in order to take key decisions affecting the economy of a country. A number of previous approaches to improve the quality of this estimation have been proposed in the last decade, the majority of them applying different machine learning techniques. In this project, we will investigate the performance of a robust hybrid approach, composed of a metaheuristic procedure and a neural network. On the one hand, the metaheuristic approach is focused on obtaining the most relevant features among the set of initial ones. We will explore how different models (linear, exponential, etc.) are suitable for this problem.

Analysis of the behavioral patterns of customers in transportation networks where there exist different alternatives for the recharging of electric/hybrid vehicles, and where the arrival rate of customers is variable.

Energy management system that integrates Smart Grids with Home Area Networks (HAN) that use smart appliance, home automation and Internet of Thing devices must address security and privacy features. That system must enable energy efficiency, energy service demand and save energy through monitoring and controlling energy in real time.

Our main research lines comprise coordination mechanisms and services for the efficient use of shared limited resources. We frequently apply them to environments with autonomous stakeholders where, besides efficiency, different types of fairness and “social welfare” as well as all sorts of “security” constraints need to be considered, so as to enable an effective implementation. For this purpose, we usually combine multiagent techniques from a sandbox that we call “agreement technologies” (semantic technologies, market-based mechanisms, automated negotiation and argumentation, trust and reputation, norms and organisations, …).

Computer Vision algorithms are a key element in order to make smart cities, transportation and mobility a reality. Our research interest are in Computer Vision in general and in efficient (low computational resources and real time) object detection and tracking in particular, with special interest in face analysis, which can applied in different problems related to smart mobility and smart cities.

Ours is a connected world. Technological (power, communications and transportation) networks play a leading role in our daily lives. To cope with the technological challenges posed by the modern society, contemporary networked systems have became more flexible, involved and autonomous. Power and transportation networks are now equipped with batteries and have access to renewable sources of energy. This evolution has opened the door to more energy efficient operation schemes and to better user’s experience. However, it has also made the design, management and operation of the networks more difficult. Successful execution of those tasks requires a detailed modeling and analysis of the network and its terminals.

Efficient planning of energy storage Energy storage is a major challenge in the long term, but also in the medium and short term. Nowadays, a lot of research is being carried out regarding energy storage from the point of view of materials science, physics, and engineering. However, from the point of view of decision support […]

Our main research lines comprise coordination mechanisms and services for the efficient use of shared limited resources. We frequently apply them to environments with autonomous stakeholders where, besides efficiency, different types of fairness and “social welfare” as well as all sorts of “security” constraints need to be considered, so as to enable an effective implementation. For this purpose, we usually combine multiagent techniques from a sandbox that we call “agreement technologies” (semantic technologies, market-based mechanisms, automated negotiation and argumentation, trust and reputation, norms and organisations, …). The ultimate goal is to provide innovative new services to the user, and at the same time to reduce CO2 emissions and energy consumption.

Our main research lines comprise coordination mechanisms and services for the efficient use of shared limited resources. We frequently apply them to environments with autonomous stakeholders where, besides efficiency, different types of fairness and “social welfare” as well as all sorts of “security” constraints need to be considered, so as to enable an effective implementation. For this purpose, we usually combine multiagent techniques from a sandbox that we call “agreement technologies” (semantic technologies, market-based mechanisms, automated negotiation and argumentation, trust and reputation, norms and organisations, …). A key application area is the smart grid. We are particularly interested in mechanisms and services that work in a “bottom-up” fashion, i.e. that provide incentives for owners to offer their resource to the grid in certain situations or at certain times, while keeping them under their control at others. We envision those service-level agreements to be negotiated, executed, and controlled in a peer-to-peer fashion.