HeartDIS: A Generalizable End-to-End Energy Disaggregation Pipeline

Ilias Dimitriadis (), Nikolaos Virtsionis Gkalinikis, Nikolaos Gkiouzelis (), Athena Vakali (), Christos Athanasiadis and Costas Baslis
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Ilias Dimitriadis: Department of Informatics, Aristotle University of Thessaloniki, 54124 Thesssaloniki, Greece
Nikolaos Virtsionis Gkalinikis: Department of Informatics, Aristotle University of Thessaloniki, 54124 Thesssaloniki, Greece
Nikolaos Gkiouzelis: Department of Informatics, Aristotle University of Thessaloniki, 54124 Thesssaloniki, Greece
Athena Vakali: Department of Informatics, Aristotle University of Thessaloniki, 54124 Thesssaloniki, Greece
Christos Athanasiadis: NET2GRID BV, Krystalli 4, 54630 Thessaloniki, Greece
Costas Baslis: Energy Management Department, Heron Energy S.A., 11526 Athens, Greece

Energies, 2023, vol. 16, issue 13, 1-27

Abstract: 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.

Keywords: energy disaggregation; energy management; data analytics; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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