UNet-NILM

Faustine, Anthony; Pereira, Lucas; Bousbiat, Hafsa; Kulkarni, Shridhar

doi:10.1145/3427771.3427859

Cited by 64 publications

(17 citation statements)

References 20 publications

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“…The vast majority of works employ either the mean absolute error (MAE) or the mean squared error (MSE) in case of power disaggregation and the cross entropy loss for on/off classification. Recent works also investigate alternative loss functions: Quantile regression [169] was employed by [46,59]. The authors of [59] found that their proposed loss increased the performance of two state-of-the-art models compared to the MSE loss.…”

Section: Training and Loss Functionsmentioning

confidence: 99%

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Review on Deep Neural Networks Applied to Low-Frequency NILM

et al. 2021

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This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e., data with sampling rates lower than the AC base frequency. The overall purpose of this review is, firstly, to gain an overview on the state of the research up to November 2020, and secondly, to identify worthwhile open research topics. Accordingly, we first review the many degrees of freedom of these approaches, what has already been done in the literature, and compile the main characteristics of the reviewed publications in an extensive overview table. The second part of the paper discusses selected aspects of the literature and corresponding research gaps. In particular, we do a performance comparison with respect to reported mean absolute error (MAE) and F1-scores and observe different recurring elements in the best performing approaches, namely data sampling intervals below 10 s, a large field of view, the usage of generative adversarial network (GAN) losses, multi-task learning, and post-processing. Subsequently, multiple input features, multi-task learning, and related research gaps are discussed, the need for comparative studies is highlighted, and finally, missing elements for a successful deployment of NILM approaches based on deep neural networks are pointed out. We conclude the review with an outlook on possible future scenarios.

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Section: Training and Loss Functionsmentioning

confidence: 99%

“…Finally, ref. [46] took multi-task learning furthest by simultaneously learning on both on/off states and active power of multiple appliances.…”

Section: Outputmentioning

confidence: 99%

Review on Deep Neural Networks Applied to Low-Frequency NILM

et al. 2021

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“…Complex loads present non-linear and variable load profiles, being easy to confuse them in aggregated measurements with other load signatures. Thus, non-conventional features have been included in recent studies as alternatives to correlate loads with external factors, complementing steady state and transient based load signatures [59]. As an example, heating and cooling loads can be correlated with operating temperatures, weather conditions, usage patterns, combinations of other loads, the season of the year, time of the day and so on.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Therefore, several techniques have been developed in order to improve the efficacy of the designed methods, which can be classified in function of the used machine learning algorithm [59]. Generally, machine learning techniques can be grouped as supervised and unsupervised methods, as well as event or non-event-based methodologies.…”

Section: Load Classificationmentioning

confidence: 99%

“…Advanced techniques based on deep learning are also used to develop NILM. Representing the operation of the human brain by defining evaluating nodes as neurons and weighted connections as axons, neural networks have been implemented in several areas of investigation [59]. Convolutional neural networks [53] and feedforward neural networks [91] represent the most common deep learning techniques used for NILM.…”

Section: Load Classificationmentioning

confidence: 99%

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Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks

et al. 2021

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Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Himeur

Alsalemi

Bensaali

et al. 2022

Int J of Intelligent Sys

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Smart nonintrusive load monitoring (NILM) represents a cost-efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy efficiency environment. This paper presents a comprehensive review of recent trends in the NILM field, in which we propose a multiperspective classification of existing smart NILM techniques. More attention is devoted to describing the contributions of deep learning, feature extraction, computing platforms, and application scenarios for NILM development. Accordingly, NILM technical aspects are first Int J Intell Syst. 2022;37:7124-7179. wileyonlinelibrary.com/journal/int investigated, including data collection devices and public data sets. Next, event-based and non-eventbased NILM algorithms are overviewed. Furthermore, potential limitations of existing solutions are identified, highlighting their technical challenges, especially those related to security and privacy preservation, data scarcity, results reproduction, and implementation and business difficulties. Lastly, future directions are explored to overcome the identified limitations.

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UNet-NILM

Cited by 64 publications

References 20 publications

Review on Deep Neural Networks Applied to Low-Frequency NILM

Review on Deep Neural Networks Applied to Low-Frequency NILM

Load modelling and non-intrusive load monitoring to integrate distributed energy resources in low and medium voltage networks

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

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