Analysis of individuals’ preference to model realistic behaviour in DSM programs
keywords AGENT BASED SIMULATION, GAME THEORY, E, HUMAN MODELS, SMART CITIES, SMART GRID, SMART HOME
Reference persons LORENZO BOTTACCIOLI, EDOARDO PATTI
Research Groups DAUIN - GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA, ELECTRONIC DESIGN AUTOMATION - EDA, Energy Center Lab, GR-06 - ELECTRONIC DESIGN AUTOMATION - EDA, ICT4SS - ICT FOR SMART SOCIETIES
Thesis type SPERIMENTAL AND SIMULATION
Description One of the main objectives of the Energy Center LAB initiative consists on designing a Multi-Energy-System (MES) Co-Simulation Platform able to qualitatively and quantitatively solve energetic transition scenarios .
Concepts such as Demand Response, Energy Aggregation, Demand Side Management are becoming popular and realistic in a Smart-cities and Smart grid scenario. Residential Demand-Side Management mechanisms (DSM) introduce the need for studying customer response to different DSM programs. Indeed, understand what could affect the user willingness to participate in these programs is crucial to obtain substantial benefits. Thus, it is important to take into account what could create discomfort to the user in order to find strategies that are welcomed by the customer. This thesis aims to understand the factors that influence users’ behaviour and their preferences in terms of appliance usage to simulate subsequently individuals. Unfortunately such data is not ready available by now. Thus, a survey should be conducted. This requires designing properly the questionnaire, choosing the best approach to consider the answers (i.e. Likert scale, online,..). After the collection of the answers, proper methods should be applied (I.e. regression analysis, clustering,..) to find if there is any kind of correlation among the feedback. Finally, the user behaviour should be modelled with proper methods in order to integrate it into a multi-agent system developed in previous works.
 P. Mancarella. MES (Multi-Energy Systems): An overview of concepts and evaluation models. Energy, 65:1–17, 2014.
Deadline 07/02/2024 PROPONI LA TUA CANDIDATURA