SPS Webinar: 16 November 2022 - EnerGAN++: A Generative Adversarial Gated Recurrent Network for Robust Energy Disaggregation

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News and Resources for Members of the IEEE Signal Processing Society

November 2022

SPS Webinar: 16 November 2022 - EnerGAN++: A Generative Adversarial Gated Recurrent Network for Robust Energy Disaggregation

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Education & Resources

Upcoming SPS Webinar!

Title: EnerGAN++: A Generative Adversarial Gated Recurrent Network for Robust Energy Disaggregation
Date: 16 November 2022
Time: 8:00 AM Eastern (New York time)
Duration: Approximately 1 Hour
Presenters: Dr. Maria Kaselimi, Dr. Nikolaos Doulamis, Dr. Athanasios Voulodimos, Dr. Anastasios Doulamis, Dr. Eftychios Protopapadakis

Based on the IEEE Xplore® article: EnerGAN++: A Generative Adversarial Gated Recurrent Network for Robust Energy Disaggregation
Published: IEEE Open Journal of Signal Processing, December 2020, available in IEEE Xplore®

Download: The original article is available for download.

 

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Abstract:

Separating the household aggregated energy consumption signal into its additive sub-components (the power signal from individual appliances), is called energy (power) disaggregation or non-intrusive load monitoring (NILM). NILM resembles the signal source separation problem and poses several challenges, not only as an ill-posed problem, but also, due to unsteady appliance signatures, abnormal behavior that is usually detected in appliances operation and the existence of noise in the aggregated signal. Currently, we are in the mature NILM period where there is an attempt for NILM to be applied in real-life application scenarios. Thus, the robustness of the algorithms, reliability, practicality, and, in general, trustworthiness are the main issues of interest.
 
In this talk, we propose the EnerGAN++, a model based on Generative Adversarial Networks for robust energy disaggregation. We attempt to unify the autoencoder (AE) and GAN architectures into a single framework, in which the autoencoder achieves a non-linear power signal source separation. EnerGAN++ is trained adversarially using a novel discriminator to enhance robustness to noise. The discriminator performs sequence classification using a recurrent convolutional neural network to handle the temporal dynamics of an appliance energy consumption time series. In particular, the proposed architecture of the discriminator leverages the ability of Convolutional Neural Networks (CNN) in rapid processing and optimal feature extraction, along with the need to infer the data temporal character and time dependence. Experimental results indicate the proposed method’s superiority compared to the current state of the art.

Biography:

Maria Kaselimi

Dr. Maria Kaselimi received the Diploma, M.Sc. and Ph.D. degrees from National Technical University of Athens (NTUA), Greece, in 2015, 2017 and 2021, respectively. She has more than 40 papers in international journals and conferences and more than 260 citations.
 
She is leader researcher in the H2020 Heart European project. Her research interest focuses on machine learning, signal processing techniques, data analysis and modeling with applications in the fields of energy disaggregation, earth monitoring and environment.
 
Dr. Kaselimi has received the 2022 Chorafas Foundation Award for outstanding work in the field of engineering sciences.

 

Nikolaos Doulamis

Dr. Nikolaos Doulamis (Member, IEEE) received the Diploma and Ph.D. degree in electrical and computer engineering from the National Technical University of Athens (NTUA) both with the highest honor.
 
He is currently an Associate Professor with the NTUA. He has received many awards (e.g., Best Student among all Engineers, Best Paper Awards) and was an Organizer and/or TPC in major IEEE conferences.
 
Dr. Doulamis has authored more than 75 (240) journals (conference) papers in the field of signal processing and machine learning and received more than 7800 citations. He has been involved in several European research projects.

 

Athanasios Voulodimos

Dr. Athanasios Voulodimos (Member, IEEE) received the Dipl.-Ing., M.Sc., and Ph.D. degrees from the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA) ranking at the top of his class.
 
He is an Assistant Professor with the School of Electrical and Computer Engineering at NTUA. From 2018 to 2021 he was an Assistant Professor at the Department of Informatics and Computer Engineering of the University of West Attica. He has been involved in several European research projects, as a Senior Researcher and a Technical Manager.
 
Dr. Voulodimos was recipient of the awards for his academic performance and scientific achievements and has coauthored more than 120 papers in international journals, conference proceedings and books in the research areas of machine learning and signal processing, including their applications in earth sciences, energy and environmental engineering, receiving more than 3000 citations.

 

Anastasios Doulamis

Dr. Anastasios Doulamis (Member, IEEE) received the Diploma and PhD degree in Electrical and Computer Engineering from the National Technical University of Athens (NTUA) with highest honor.
 
Until January 2014, he was an Associate Professor at the Technical University of Crete and now is an Assistant Professor at NTUA. He has received several awards in his studies, including the Best Greek Student Engineer, Best Graduate Thesis Award.
 
Dr. Doulamis has also served as program committee in several major conferences of IEEE and ACM. He is author of more than 350 papers in leading journals and conferences receiving more than 7541 citations.

 

Eftychios Protopapadakis

Dr. Eftychios Protopapadakis studied production engineering management at technical university of Crete. His educational background includes a M.Sc. degree in management and business administrator, a Ph.D. in decision systems, both at the same university and a post–Ph.D. in semi–supervised deep learning models, at National Technical University of Athens. He has worked as an engineer in European (BENEFFICE, euPOLIS, eVACUATE, PANOPTIS, PHOOTONICS) projects since 2010.
 
His research interests focus on machine learning applications. He has explored the applicability of semi-supervised techniques in various applications. Other investigated areas involve stock market share trends’ forecasting and credit risk assessment. Additionally, he has worked on structural assessment in transportation tunnel infrastructures, via deep–learning techniques and robotic platforms.
 
Dr. Protopapadakis co–authored more than 80 publications, receiving more than 3400 citations. His paper on industrial workflow recognition received the best paper award in INFOCOMP 2012. Other awards include university scholarships for excellence, Technical Chamber of Greece award for excellence in studies, state scholarship foundation – SIEMENS doctorate scholarship 2012 and post PhD scholarship of the National Strategic Reference Framework 2014–2020.

 

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