Frequency Invariant Transformation of Periodic Signals (FIT-PS) for Classification in NILM

Held, Pirmin; Mauch, Steffen; Saleh, Alaa; Abdeslam, Djaffar Ould; Benyoucef, Dirk

doi:10.1109/tsg.2018.2886849

Cited by 20 publications

(9 citation statements)

References 18 publications

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“…A noteworthy example can be found in [192], where the original current waveform, serving as a NILM signature, undergoes a frequency-invariant transformation. This transformation, based on periodic signals, aligns uncorrelated sample data with a fixed multiple of the grid frequency.…”

Section: ) Frequency Feature Extractionmentioning

confidence: 99%

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Liu

Wang

You

2019

IEEE Trans. Smart Grid

169

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Non-Intrusive Load Monitoring (NILM) is pivotal in today's energy landscape, offering vital solutions for energy conservation and efficient management. Its growing importance in enhancing energy savings and understanding consumer behavior makes it a pivotal technology for addressing global energy challenges. This paper delivers an in-depth review of NILM, highlighting its critical role in smart homes and smart grids. The significant contributions of this study are threefold: Firstly, it compiles a comprehensive global dataset table, providing a valuable tool for researchers and engineers to select appropriate datasets for their NILM studies. Secondly, it categorizes NILM approaches, simplifying the understanding of various algorithms by focusing on technologies, label data requirements, feature usage, and monitoring states. Lastly, by identifying gaps in current NILM research, this work sets a clear direction for future studies, discussing potential areas of innovation.

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Section: ) Frequency Feature Extractionmentioning

confidence: 99%

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Liu

Wang

You

2019

IEEE Trans. Smart Grid

169

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“…In detail, selection of odd harmonic current vectors for identification of variable power loads and power electronics has been presented in [47], [48]. Furthermore, a frequency invariant transformation for periodic current signals has been presented in [49] converting uncorrelated samples to multiples of the grid frequency. Moreover, the approach in [50] uses odd current harmonics in combination with a transient event detection stage and utilizes a bipartite graph matching problem for disaggregation.…”

Section: Introductionmentioning

confidence: 99%

Double Fourier Integral Analysis Based Convolutional Neural Network Regression for High-Frequency Energy Disaggregation

Schirmer

Mporas

2022

IEEE Trans. Emerg. Top. Comput. Intell.

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Non-Intrusive Load Monitoring aims to extract the energy consumption of individual electrical appliances through disaggregation of the total power load measured by a single smart-meter. In this article we introduce Double Fourier Integral Analysis in the Non-Intrusive Load Monitoring task in order to provide more distinct feature descriptions compared to current or voltage spectrograms. Specifically, the high-frequency aggregated current and voltage signals are transformed into two-dimensional unit cells as calculated by Double Fourier Integral Analysis and used as input to a Convolutional Neural Network for regression. The performance of the proposed methodology was evaluated in the publicly available UK-DALE dataset. The proposed approach improves the estimation accuracy by 7.2% when compared to the baseline energy disaggregation setup using current and voltage spectrograms.

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“…Besides using raw time series [14], several works [34], [35] consider frequency domain or other transformations that typically preserve the dimensionality. However, more recently time-series to image transformations that expand the 1D time series into a 2D image have been considered.…”

Section: B Dimensionality Expansion Through Time Series To Image Tran...mentioning

confidence: 99%

Dimensionality Expansion of Load Monitoring Time Series and Transfer Learning for EMS

Bertalanič¹,

Jenko²,

Fortuna³

2022

Preprint

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Electrical management systems (EMS) are playing a central role in enabling energy savings. They can be deployed within an everyday household where they monitor and manage appliances and help residents be more energy efficient and subsequently also more economical. One of they key functionalities of EMS is to automatically detect and identify appliances within a household through the process of load monitoring. In this paper, we propose a new transfer learning approach for building EMS (BEMS) and study the trade-offs in terms of numbers of samples and target classes in adapting a backbone model during the transfer process. We also perform a first time analysis of feature expansion through video-like transformation of time series data for device classification in non intrusive load monitoring (NILM) and propose a deep learning architecture enabling accurate appliance identification. We examine the relative performance of our method on 5 different representative low-frequency datasets and show that our method performs with an average F1 score of 0.88 on these datasets.

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Frequency Invariant Transformation of Periodic Signals (FIT-PS) for Classification in NILM

Cited by 20 publications

References 18 publications

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Double Fourier Integral Analysis Based Convolutional Neural Network Regression for High-Frequency Energy Disaggregation

Dimensionality Expansion of Load Monitoring Time Series and Transfer Learning for EMS

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