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Review

Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring

1
School of Rural and Surveying Engineering, National Technical University of Athens, 15773 Athens, Greece
2
School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece
*
Author to whom correspondence should be addressed.
Academic Editor: Marco Leo
Sensors 2022, 22(15), 5872; https://doi.org/10.3390/s22155872
Received: 13 July 2022 / Revised: 29 July 2022 / Accepted: 2 August 2022 / Published: 5 August 2022
(This article belongs to the Section Intelligent Sensors)
Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components. Over the years, signal processing and machine learning algorithms have been combined to achieve this. Many publications and extensive research works are performed on energy disaggregation or NILM for the state-of-the-art methods to reach the desired performance. The initial interest of the scientific community to formulate and describe mathematically the NILM problem using machine learning tools has now shifted into a more practical NILM. 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 complexity of the algorithms, transferability, reliability, practicality, and, in general, trustworthiness are the main issues of interest. This review narrows the gap between the early immature NILM era and the mature one. In particular, the paper provides a comprehensive literature review of the NILM methods for residential appliances only. The paper analyzes, summarizes, and presents the outcomes of a large number of recently published scholarly articles. Furthermore, the paper discusses the highlights of these methods and introduces the research dilemmas that should be taken into consideration by researchers to apply NILM methods. Finally, we show the need for transferring the traditional disaggregation models into a practical and trustworthy framework. View Full-Text
Keywords: nonintrusive load monitoring; energy disaggregation; machine learning; signal processing; review nonintrusive load monitoring; energy disaggregation; machine learning; signal processing; review
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MDPI and ACS Style

Kaselimi, M.; Protopapadakis, E.; Voulodimos, A.; Doulamis, N.; Doulamis, A. Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring. Sensors 2022, 22, 5872. https://doi.org/10.3390/s22155872

AMA Style

Kaselimi M, Protopapadakis E, Voulodimos A, Doulamis N, Doulamis A. Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring. Sensors. 2022; 22(15):5872. https://doi.org/10.3390/s22155872

Chicago/Turabian Style

Kaselimi, Maria, Eftychios Protopapadakis, Athanasios Voulodimos, Nikolaos Doulamis, and Anastasios Doulamis. 2022. "Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring" Sensors 22, no. 15: 5872. https://doi.org/10.3390/s22155872

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