Publications de Matteo Vasirani

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Wind power is gaining in significance as an important renewable source of clean energy. However, due to their inherent uncertainty, wind generators are often unable to participate in the forward electricity markets like the more predictable and controllabl ...

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 consu ...

Demand response (DR) is a Smart Grid application that aims at managing consumption of electricity in response to supply-side signals. The exchange of sensitive information (i.e., electricity consumption data) between the consumers and the DR provider is ne ...

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 an ...

The recent development of smart meters has allowed the analysis of household electricity consumption in real time. Predicting electricity consumption at such very low scales should help to increase the efficiency of distribution networks and energy pricing ...

Demand response (DR) emerges as one of the cheapest and greenest solutions to match supply and demand in the electricity sector. While DR has been focused on large and industrial consumers, pervasive implementation (by including residential consumers) is n ...

The richness of user-centric information gathered by modern devices can be used to keep track of memorable events, therefore acting as a prosthesis of the prone-to-forget human memory. We propose to combine virtual and physical sensors from mobile devices ...

Modern smartphones are powerful platforms that have become part of the everyday life for most people. Thanks to their sensing and computing capabilities, smartphones can unobtrusively identify simple user states (e.g., location, performed activity, etc.), ...

Demand response (DR) has been known to play an important role in the electricity sector to balance supply and demand. To this end, the DR baseline is a key factor in a successful DR program since it influences the incentive allocation mechanism and custome ...

While electricity demand forecasting literature has focused on large, industrial, and national demand, this paper focuses on short-term (1 and 24 hour ahead) electricity demand forecasting for residential customers at the individual and aggregate level. Si ...

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 ...

Matteo Vasirani

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An Agent-Based Approach to Virtual Power Plants of Wind Power Generators and Electric Vehicles

Decentralized Planning of Energy Demand for the Management of Robustness and Discomfort

Privacy Enhanced Demand Response with Reputation-based Incentive Distribution

A Cloud-Based eHealth Architecture for Privacy Preserving Data Integration

Electricity Load Forecasting for Residential Customers: Exploiting Aggregation and Correlation between Households

An Economic Analysis of Pervasive, Incentive-Based Demand Response

MemorySense: Reconstructing and Ranking User Memories on Mobile Devices

RoutineSense: A Mobile Sensing Framework for the Reconstruction of User Routines

When Bias Matters: An Economic Assessment of Demand Response Baselines for Residential Customers

Cluster-based Aggregate Forecasting for Residential Electricity Demand using Smart Meter Data

SmartD: Smart Meter Data Analytics Dashboard

A Cost-Benefit Analysis of Data Processing Architectures for the Smart Grid

Performance Analysis of Data Processing Architectures for the Smart Grid

Measuring and Controlling Unfairness in Decentralized Planning of Energy Demand

Special Issue on Intelligent Agents in Traffic and Transportation

Evaluating Demand Response Programs: Getting the Key Performance Indicators Right

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