Eftychios Protopapadakis — accepted AI-conference papers

2021

A Robust to Noise Adversarial Recurrent Model for Non-Intrusive Load Monitoring

Maria Kaselimi, Athanasios Voulodimos, Nikolaos Doulamis, Anastasios D. Doulamis, Eftychios Protopapadakis

ICASSP 2021accepted

The problem of separating the household aggregated power signal into its additive sub-components, called energy (power) disaggregation or Non-Intrusive Load Monitoring (NILM) can play an instrumental role as a driver towards consumer energy consumption awareness and behavioral change. In this paper,…

Cited by 0 Source ↗Scholar ↗

2020

EnerGAN: A GENERATIVE ADVERSARIAL NETWORK FOR ENERGY DISAGGREGATION

Maria Kaselimi, Athanasios Voulodimos, Eftychios Protopapadakis, Nikolaos Doulamis, Anastasios D. Doulamis

ICASSP 2020accepted

An efficient, appliance-level approach for energy disaggregation, exploiting the benefits of Generative Adversarial Networks, is presented. The concept of adversarial training supports the creation of fine tuned dissagregators, which produce more detailed load estimations for a specific appliance, c…

Cited by 0 Source ↗Scholar ↗

2019

Bayesian-optimized Bidirectional LSTM Regression Model for Non-intrusive Load Monitoring

Maria Kaselimi, Nikolaos Doulamis, Anastasios D. Doulamis, Athanasios Voulodimos, Eftychios Protopapadakis

ICASSP 2019accepted

In this paper, a Bayesian-optimized bidirectional Long Short -Term Memory (LSTM) method for energy disaggregation, is introduced. Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), is a process aiming to identify the individual contribution of appliances in the aggregate electricity loa…

Cited by 0 Source ↗Scholar ↗