In typical Renewable Energy Communities (RECs), members are mostly residential users and only aggregated data on electrical consumption is available. Specific consumption for heating and cooling is not monitored separately, which limits the assessment of future potential for RECs, such as providing grid flexibility services. This thesis project proposes the use of Nonintrusive Load Monitoring (NILM) techniques – belonging to the Advanced Data Analytics family – to identify and isolate energy consumption attributable to heating and cooling, improving the management and efficiency of RECs.
The student will benefit from synergies with LEAP – Laboratorio Energia e Ambiente Piacenza, already active on several projects on the topic.
Area di competenza
Smart Energy Systems
Responsabili
Filippo Bovera (filippo.bovera@polimi.it) – Politecnico di Milano
Matteo Zatti (matteo.zatti@polimi.it) – LEAP
Gaia Martoriello (gaia.martoriello@polimi.it) – LEAP
Marco Gabba (marco.gabba@polimi.it) – LEAP
Competenze richieste
Energy communities, machine learning, electricity markets
Background in Energy/Electrical Engineering
Data di inizio presunta
As soon as possible
Durata
9-12 months (full-thesis). Short thesis will be considered if requested.
Maggiori informazioni
Weekly meeting online or in presence.