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Deep Learning techniques for Non-Intrusive Load Monitoring

azienda Thesis in external company    


keywords ARTIFICIAL INTELLIGENCE, ARTIFICIAL NEURAL NETWORKS, DEEP NEURAL NETWORKS, SMART GRID

Reference persons EDOARDO PATTI

External reference persons Marco Castangia (marco.castangia@polito.it), Christian Camarda (christian@midorisrl.eu)

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 EXPERIMENTAL

Description Non-Intrusive Load Monitoring (NILM) is a technique that enables to separate the electrical loads of individual appliances starting from the total electrical load of the household.
In the past decade, deep learning techniques proved particularly effective in detecting and extracting the power consumption of several devices from the aggregate power consumptions.
However, due to the costs and labor efforts involved in collecting device-specific power consumptions, researchers experienced a widespread lack of annotated data to train deep learning models.
This thesis is aimed at developing innovative data augmentation techniques that can improve the performance of deep learning models in the context of NILM.
For this purpose, the student will also implement state-of-the-art deep learning models in order to test and evaluate the proposed data augmentation methodology.

Required skills Python


Deadline 07/08/2021      PROPONI LA TUA CANDIDATURA




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