Matteo Vasirani

Agent-Based Semantic Service Discovery for Healthcare: An Organizational Approach

Information and communication technologies offer great potential for society to quickly adopt e-services for economic and social development. Healthcare activities based on these technologies (e-health) are probably the most prominent of these e-services. However, e-health is evolving into such entities as m-health (mobile) or u-health (ubiquitous), which focus on applications that provide healthcare to people anywhere, anytime using broadband and wireless mobile technologies. This semantic-service-discovery mechanism considers relevant parts of the organizational context in which e-health services are used, to improve a service-discovery systems usability in medical emergencies

Dynamic coalition adaptation for efficient agent-based virtual power plants

An agent-based organizational model for a smart energy system is introduced relying on a dynamic coalition formation mechanism for virtual power plants. Central to this mechanism we propose a solution concept that stems from the existent stability notions in coalitional games. The process is intended as an open-ended organizational adaptation, concerned with achieving stable configurations that meet the desired functionalities within stochastic scenarios. We deploy the mechanism in distributed environments populated by negotiating agents and give empirical results that prove a significant improvement of organizational efficiency.

Decentralized planning of energy demand for the management of robustness and discomfort

The robustness of smart grids is challenged by unpredictable power peaks or temporal demand oscillations that can cause blackouts and increase supply costs. Planning of demand can mitigate these effects and increase robustness. However, the impact on consumers in regards to the discomfort they experience as a result of improving robustness is usually neglected. This paper introduces a decentralized agent-based approach that quantifies and manages the tradeoff between robustness and discomfort under demand planning. Eight selection functions of plans are experimentally evaluated using real data from two operational smart grids. These functions can provide different quality of service levels for demand-side energy self-management that capture both robustness and discomfort criteria.

Smart consumer load balancing: state of the art and an empirical evaluation in the Spanish electricity market

The basis of an efficient functioning of a power grid is an accurate balancing of the electricity demand of all the consumers at any instant with supply. Nowadays, this task involves only the grid operator and retail electricity providers. One of the facets of the Smart Grid vision is that consumers may have a more active role in the problem of balancing demand with supply. With the deployment of intelligent information and communication technologies in domestic environments, homes are becoming smarter and able to play a more active role in the management of energy. We use the term Smart Consumer Load Balancing to refer to algorithms that are run by energy management systems of homes in order to optimise the electricity consumption, to minimise costs and/or meet supply constraints. In this work, we analyse different approaches to Smart Consumer Load Balancing based on (distributed) artificial intelligence. We also put forward a new model of Smart Consumer Load Balancing, where consumers actively participate in the balancing of demand with supply by forming groups that agree on a joint demand profile to be contracted in the market with the mediation of an aggregator. We specify the business model as well as the optimisation model for load balancing, showing the economic benefits for the consumers in a realistic scenario based on the Spanish electricity market.

SmartD: smart meter data analytics dashboard

The ability of smart meters to communicate energy consumption data in (near) real-time enables data analytics for novel applications, such as pervasive demand response, personalized energy feedback, outage management, and theft detection. Smart meter data are characterized by big volume and big velocity, which make processing and analysis very challenging from a computational point of view. In this paper we presented SmartD, a dashboard that enables the data analyst to visualize smart meter data and estimate the typical load profile of new consumers according to different contexts, temporal aggregations and consumer segments.

A role-based support mechanism for service description and discovery

The ever-growing number of services on the WWW provides enormous business opportunities. Services can be automatically discovered and invoked, or even be dynamically composed from more simples ones. In this paper we concentrate on the problem of service discovery. Most current approaches base their search on inputs and outputs of the service. Some of them also take into account preconditions and effects, and other parameters that describe the service. We present a new approach that complements existing ones by considering the types of interactions that services can be used in. We present our proposal for a concrete application based on a real-world scenario for emergency assistance in the healthcare domain

Centralized and decentralized balance group optimization in electricity markets with demand response

In this paper, the potential of using Demand Response (DR) to minimize balancing energy costs of Balance Groups (BGs) in electricity markets is investigated. Two algorithms are developed based on direct and price-based control concepts, respectively, to control an aggregated pool of office buildings. The direct control algorithm is set up as a centralized Model Predictive Control (MPC) problem yielding an optimal control sequence. This is used as a benchmark for a decentralized price control scheme, which is suboptimal, but still provides a good performance with much lower communication requirements compared to the benchmark. The two approaches are compared using a case study and conclusions regarding their advantages and disadvantages are drawn based on simulation results. The results show that with proper exploitation of the flexibility of office building aggregations significant balancing cost reductions can be achieved with only limited communication which is, in particular, respecting privacy requirements.

Collective-Based Multiagent Coordination: A Case Study

In this paper we evaluate Probability Collectives (PC) as a framework for the coordination of collectives of agents. PC allows for efficient multiagent coordination without the need of explicit acquaintance models. We selected Distributed Constraint Satisfaction as case study to evaluate the PC approach for the well-known 8-Queens problem. Two different architectural structures have been implemented, one centralized and one decentralized. We have also compared between the decentralized version of PC and ADOPT, the state of the art in distributed constraint satisfaction algorithms.

A Cloud-Based eHealth Architecture for Privacy Preserving Data Integration

In this paper, we address the problem of building an anonymized medical database from multiple sources. Our proposed solution defines how to achieve data integration in a heterogeneous network of many clinical institutions, while preserving data utility and patients’ privacy. The contribution of the paper is twofold: Firstly, we propose a secure and scalable cloud eHealth architecture to store and exchange patients’ data for the treatment. Secondly, we present an algorithm for efficient aggregation of the health data for the research purposes from multiple sources independently.

Measuring and controlling unfairness in decentralized planning of energy demand

Demand-side energy management improves robustness and efficiency in Smart Grids. Load-adjustment and load-shifting are performed to match demand to available supply. These operations come at a discomfort cost for consumers as their lifestyle is influenced when they adjust or shift in time their demand. Performance of demand-side energy management mainly concerns how robustness is maximized or discomfort is minimized. However, measuring and controlling the distribution of discomfort as perceived between different consumers provides an enriched notion of fairness in demand-side energy management that is missing in current approaches. This paper defines unfairness in demand-side energy management and shows how unfairness is measurable and controllable by software agents that plan energy demand in a decentralized fashion. Experimental evaluation using real demand and survey data from two operational Smart Grid projects confirms these findings.