Nikolaos Gkiouzelis
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Interests
Nikolaos Gkiouzelis has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Athena Vakali
Aristotle University of Thessaloniki
1 shared publication - Christos L. Athanasiadis
Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
1 shared publication - Costas Baslis
Energy Management Department, Heron Energy S.A., 11526 Athens, Greece
1 shared publication - Ilias Dimitriadis
Aristotle University of Thessaloniki, Thessaloniki, Greece
1 shared publication - Nikolaos Virtsionis Gkalinikis
School of Informatics, Aristotle University of Thessaloniki, 54124 Thesssaloniki, Greece
1 shared publication
Latest Publications
- Open Access
HeartDIS: A Generalizable End-to-End Energy Disaggregation Pipeline
The need for a more energy-efficient future is now more evident than ever. Energy disagreggation (NILM) methodologies have been proposed as an effective solution for the reduction in energy consumption. However, there is a wide range of challenges that NILM faces that still have not been addressed. Herein, we propose HeartDIS, a generalizable energy disaggregation pipeline backed by an extensive set of experiments, whose aim is to tackle the performance and efficiency of NILM models with respect to the available data. Our research (i) shows that personalized machine learning models can outperform more generic models; (ii) evaluates the generalization capabilities of these models through a wide range of experiments, highlighting the fact that the combination of synthetic data, the decreased volume of real data, and fine-tuning can provide comparable results; (iii) introduces a more realistic synthetic data generation pipeline based on other state-of-the-art methods; and, finally, (iv) facilitates further research in the field by publicly sharing synthetic and real data for the energy consumption of two households and their appliances.
Open AccessHeartDIS: A Generalizable End-to-End Energy Disaggregation Pipeline
The need for a more energy-efficient future is now more evident than ever. Energy disagreggation (NILM) methodologies have been proposed as an effective solution for the reduction in energy consumption. However, there is a wide range of challenges that NILM faces that still have not been addressed. Herein, we propose HeartDIS, a generalizable energy disaggregation pipeline backed by an extensive set of experiments, whose aim is to tackle the performance and efficiency of NILM models with respect to the available data. Our research (i) shows that personalized machine learning models can outperform more generic models; (ii) evaluates the generalization capabilities of these models through a wide range of experiments, highlighting the fact that the combination of synthetic data, the decreased volume of real data, and fine-tuning can provide comparable results; (iii) introduces a more realistic synthetic data generation pipeline based on other state-of-the-art methods; and, finally, (iv) facilitates further research in the field by publicly sharing synthetic and real data for the energy consumption of two households and their appliances.