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Machine learning for anomalous detection of household appliances

azienda Tesi esterna in azienda    


Parole chiave ANOMALY DETECTION, ARTIFICIAL INTELLIGENCE, ARTIFICIAL NEURAL NETWORKS, DEEP NEURAL NETWORKS, MACHINE LEARNING, NON-INTRUISIVE LOAD MONITORING, SMART GRIDS

Riferimenti EDOARDO PATTI

Riferimenti esterni Marco Castangia (marco.castangia@polito.it), Christian Camarda (christian@midorisrl.eu)

Gruppi di ricerca 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

Tipo tesi SPERIMENTALE

Descrizione The detection of anomalous behaviors in the power consumption of appliances can help in reducing energy wastage in households. Malfunctioning appliances usually show a power signature statistically different from their normal behavior, which can lead to higher energy consumption or more serious damages. Alternatively, anomalous behaviors can be caused by negligent users exhibiting bad habits in the usage of their appliances. This thesis aims at detecting anomalous behaviors in the power consumption of various appliances by means of machine learning techniques for anomaly detection. In detail, the candidate will analyze the power consumption of different appliances in order to detect significant outliers. The ideal candidate should be familiar with Python. The knowledge of basic machine learning algorithms is a plus.

Conoscenze richieste python programming language


Scadenza validita proposta 10/02/2022      PROPONI LA TUA CANDIDATURA




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