Multi Label Restricted Boltzmann Machine for Non-intrusive Load Monitoring

Verma, Sagar; Singh, Shikha; Majumdar, Angshul

doi:10.1109/icassp.2019.8682860

Cited by 18 publications

(10 citation statements)

References 12 publications

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“…Publications that use shallow neural networks with only a single hidden layer such as, e.g., [27][28][29], are not included in our review. We restricted ourselves to approaches that train neural networks with back-propagation, excluding alternative approaches such as, e.g., [30,31]. Since the scope involves DNNs and NILM, we assume that the reader is familiar with the general concepts of the two fields, and we will merely introduce the basic NILM problem formulation in Section 2.1.…”

Section: Scopementioning

confidence: 99%

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: Scopementioning

confidence: 99%

Review on Deep Neural Networks Applied to Low-Frequency NILM

et al. 2021

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“…Another group of works that are relevant to this study are those taking NILM as a multi-label classification problem (e.g., [4,5,32,43,45,47]). These works make use of annotated aggregated load for model training, which contains information on the on/off state of the target devices.…”

Section: Related Workmentioning

confidence: 99%

“…An early study investigating multi-label classification methods for NILM was presented in [5], which provided a comparison of several multi-label classification algorithms such as multi-label K nearest neighbour and random K label-sets. More recent works are based on multi-label deep learning [43], multi-label graph learning [32], multi-label restricted Boltzmann machine [47]. In this work, multi-label classification is realised by training a model for each appliance to identify if the appliance is in an on/off state.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Energy Disaggregation and On/Off Detection of Household Appliances

Jiang

Kong

Plumbley

et al. 2021

ACM Trans. Knowl. Discov. Data

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Energy disaggregation, a.k.a. Non-Intrusive Load Monitoring, aims to separate the energy consumption of individual appliances from the readings of a mains power meter measuring the total energy consumption of, e.g., a whole house. Energy consumption of individual appliances can be useful in many applications, e.g., providing appliance-level feedback to the end users to help them understand their energy consumption and ultimately save energy. Recently, with the availability of large-scale energy consumption datasets, various neural network models such as convolutional neural networks and recurrent neural networks have been investigated to solve the energy disaggregation problem. Neural network models can learn complex patterns from large amounts of data and have been shown to outperform the traditional machine learning methods such as variants of hidden Markov models. However, current neural network methods for energy disaggregation are either computational expensive or are not capable of handling long-term dependencies. In this article, we investigate the application of the recently developed WaveNet models for the task of energy disaggregation. Based on a real-world energy dataset collected from 20 households over 2 years, we show that WaveNet models outperforms the state-of-the-art deep learning methods proposed in the literature for energy disaggregation in terms of both error measures and computational cost. On the basis of energy disaggregation, we then investigate the performance of two deep-learning based frameworks for the task of on/off detection which aims at estimating whether an appliance is in operation or not. The first framework obtains the on/off states of an appliance by binarising the predictions of a regression model trained for energy disaggregation, while the second framework obtains the on/off states of an appliance by directly training a binary classifier with binarised energy readings of the appliance serving as the target values. Based on the same dataset, we show that for the task of on/off detection the second framework, i.e., directly training a binary classifier, achieves better performance in terms of F1 score.

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“…Besides an extensive survey of the topic, the authors present the multi-label meta-classification framework (RAkEL) and the bespoke multi-label classification algorithm (MLkNN), where both employ time-domain and wavelet-domain feature sets. Other approaches to multi-label NILM comprise restricted Boltzmann machines [31], and multi-target classification [32].…”

Section: Related Workmentioning

confidence: 99%

Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

Faustine

Pereira²

2020

Energies

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The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.

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Multi Label Restricted Boltzmann Machine for Non-intrusive Load Monitoring

Cited by 18 publications

References 12 publications

Review on Deep Neural Networks Applied to Low-Frequency NILM

Review on Deep Neural Networks Applied to Low-Frequency NILM

Deep Learning-Based Energy Disaggregation and On/Off Detection of Household Appliances

Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

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