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A Data-Centric Analysis of the Impact of Non-Electric Data on the Performance of Load Disaggregation Algorithms
 
 
Article

2D Transformations of Energy Signals for Energy Disaggregation

by 1,2,*,† and 1,†
1
School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK
2
Department of Power Electronics, BMW AG, 80788 Munich, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Lucas Pereira and Lina Stankovic
Sensors 2022, 22(19), 7200; https://doi.org/10.3390/s22197200
Received: 21 August 2022 / Revised: 18 September 2022 / Accepted: 20 September 2022 / Published: 22 September 2022
The aim of Non-Intrusive Load Monitoring is to estimate the energy consumption of individual electrical appliances by disaggregating the overall power consumption that has been sampled from a smart meter at a house or commercial/industrial building. Last decade’s developments in deep learning and the utilization of Convolutional Neural Networks have improved disaggregation accuracy significantly, especially when utilizing two-dimensional signal representations. However, converting time series’ to two-dimensional representations is still an open challenge, and it is not clear how it influences the performance of the energy disaggregation. Therefore, in this article, six different two-dimensional representation techniques are compared in terms of performance, runtime, influence on sampling frequency, and robustness towards Gaussian white noise. The evaluation results show an advantage of two-dimensional imaging techniques over univariate and multivariate features. In detail, the evaluation results show that: first, the active and reactive power-based signatures double Fourier based signatures, as well as outperforming most of the other approaches for low levels of noise. Second, while current and voltage signatures are outperformed at low levels of noise, they perform best under high noise conditions and show the smallest decrease in performance with increasing noise levels. Third, the effect of the sampling frequency on the energy disaggregation performance for time series imaging is most prominent up to 1.2 kHz, while, above 1.2 kHz, no significant improvements in terms of performance could be observed. View Full-Text
Keywords: appliance identification; Non-Intrusive Load Monitoring (NILM); time series imaging; two-dimensional signal representations appliance identification; Non-Intrusive Load Monitoring (NILM); time series imaging; two-dimensional signal representations
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MDPI and ACS Style

Schirmer, P.A.; Mporas, I. 2D Transformations of Energy Signals for Energy Disaggregation. Sensors 2022, 22, 7200. https://doi.org/10.3390/s22197200

AMA Style

Schirmer PA, Mporas I. 2D Transformations of Energy Signals for Energy Disaggregation. Sensors. 2022; 22(19):7200. https://doi.org/10.3390/s22197200

Chicago/Turabian Style

Schirmer, Pascal A., and Iosif Mporas. 2022. "2D Transformations of Energy Signals for Energy Disaggregation" Sensors 22, no. 19: 7200. https://doi.org/10.3390/s22197200

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