Identification of Electrical Appliances Using Their Virtual Description and Data Selection for Non-Intrusive Load Monitoring

Bartman, Jacek; Kwater, Tadeusz

doi:10.1109/tce.2021.3135423

Cited by 7 publications

(3 citation statements)

References 24 publications

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“…In studies such as [173], adaptive linear neurons have been employed to calculate the magnitude and phase of each frequency component based on fundamental and harmonic frequencies. Typically, harmonic components of different orders, such as third, fifth, and seventh, are utilized for load signature detection [165], [174]. It is crucial to investigate the non-intrusive identification of harmonic loads from a safety perspective to prevent the injection of undesirable harmonics into the system [163], [167].…”

Section: … …mentioning

confidence: 99%

“…Phase noise, a unique characteristic of electrical appliances, has also been adopted in [176] to extract the electrical energy consumption of individual appliances from the aggregate load. Furthermore, various statistical characteristics, such as start-up current [164], current total harmonic distortion (THD) [165], crest factor [165], form factor [165], and rise/fall time [171], learned from transient voltage or current, can significantly enhance the accuracy of NILM when combined with different algorithms.…”

Section: … …mentioning

confidence: 99%

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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: … …mentioning

confidence: 99%

Section: … …mentioning

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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“…On the other hand, it can be regarded as a binary classification problem if the task is to determine the on/off state of appliance i at time t, based on the aggregate signal y(t). Formulated in this manner, NILM can be solved in a range of supervised and unsupervised approaches and eliminates the need for appliance submetering, leading to a reduction in costs [28], while still enabling a diverse set of applications such as energy usage feedback [29], anomaly detection [30], and load shifting [31].…”

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confidence: 99%

Toward Transparent Load Disaggregation—A Framework for Quantitative Evaluation of Explainability Using Explainable AI

Batic,

Stankovic,

Stankovic

2024

IEEE Trans. Consumer Electron.

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Load Disaggregation, or Non-intrusive Load Monitoring (NILM), refers to the process of estimating energy consumption of individual domestic appliances from aggregated household consumption. Recently, Deep Learning (DL) approaches have seen increased adoption in NILM community. However, DL NILM models are often treated as blackbox algorithms, which introduces algorithmic transparency and explainability concerns, hindering wider adoption. Recent works have investigated explainability of DL NILM, however they are limited to computationally expensive methods or simple classification problems. In this work, we present a methodology for explainability of regression-based DL NILM with visual explanations, using explainable AI (XAI). Two explainability levels are provided. Sequence-level explanations highlight important features of predicted time-series sequence of interest, while pointlevel explanations enable visualising explanations at a point in time. To facilitate wider adoption of XAI, we define desirable properties of NILM explanations -faithfulness, robustness and effective complexity. Addressing the limitation of existing XAI NILM approaches that don't assess the quality of explanations, desirable properties of explanations are used for quantitative evaluation of explainability. We show that proposed framework enables better understanding of NILM outputs and helps improve design by providing a visualization strategy and rigorous evaluation of quality of XAI methods, leading to transparency of outcomes.

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Hollow village detection method based on non-intrusive power load monitoring

Wang

Chen

et al. 2023

Energy Reports

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Identification of Electrical Appliances Using Their Virtual Description and Data Selection for Non-Intrusive Load Monitoring

Cited by 7 publications

References 24 publications

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

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

Toward Transparent Load Disaggregation—A Framework for Quantitative Evaluation of Explainability Using Explainable AI

Hollow village detection method based on non-intrusive power load monitoring

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