Context-Based Energy Disaggregation in Smart Homes

Paradiso, Francesca; Paganelli, Federica; Giuli, Dino; Capobianco, Samuele

doi:10.3390/fi8010004

Cited by 43 publications

(28 citation statements)

References 34 publications

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“…A magnitude-base unsupervised NALM approach presented VOLUME 4, 2016 in [29] uses standard HMM with smoothing to obtain better features. Two FHMM-based approaches in [30] and [31] exploit context information and interactions chains, respectively, to improve performances of standard FHMM.…”

Section: Low-rate Non-intrusive Appliance Load Monitoring (Nalm):mentioning

confidence: 99%

On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing

2016

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With ongoing large-scale smart energy metering deployments worldwide, disaggregation of a household's total energy consumption down to individual appliances using analytical tools, also known as non-intrusive appliance load monitoring (NALM), has generated increased research interest lately. NALM can deepen energy feedback, support appliance retrofit advice, and support home automation. However, despite the fact that NALM was proposed over 30 years ago, there are still many open challenges with respect to its practicality and effectiveness at low sampling rates. Indeed, the majority of NALM approaches, supervised or unsupervised, require training to build appliance models, and are sensitive to appliance changes in the house, thus requiring regular re-training. In this paper, we tackle this challenge by proposing an NALM approach that does not require any training. The main idea is to build upon the emerging field of graph signal processing to perform adaptive thresholding, signal clustering, and pattern matching. We determine the performance limits of our approach and demonstrate its usefulness in practice. Using two open access datasets-the US REDD data set with active power measurements downsampled to 1 min resolution and the UK REFIT data set with 8-s resolution, we demonstrate the effectiveness of the proposed method for typical smart meter sampling rate, with the state-of-the-art supervised and unsupervised NALM approaches as benchmarks. 1 INDEX TERMS Non-intrusive appliance load monitoring, load disaggregation, graph signal processing.

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Section: Low-rate Non-intrusive Appliance Load Monitoring (Nalm):mentioning

confidence: 99%

On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing

2016

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“…The inference was carried out by Particle Filtering (PF). Paradiso et al [55] proposed a new electrical load disaggregation system, which utilizes FHMMs and explores context-based features. The user presence and the power patterns represent the context data.…”

Section: Related Workmentioning

confidence: 99%

An Extreme Learning Machine Approach to Effective Energy Disaggregation

Salerno

Rabbeni

2018

Electronics

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Power disaggregation is aimed at determining appliance-by-appliance electricity consumption, leveraging upon a single meter only, which measures the entire power demand. Data-driven procedures based on Factorial Hidden Markov Models (FHMMs) have produced remarkable results on energy disaggregation. Nevertheless, these procedures have various weaknesses; there is a scalability problem as the number of devices to observe rises, and the inference step is computationally heavy. Artificial neural networks (ANNs) have been demonstrated to be a viable solution to deal with FHMM shortcomings. Nonetheless, there are two significant limitations: A complicated and time-consuming training system based on back-propagation has to be employed to estimate the neural architecture parameters, and large amounts of training data covering as many operation conditions as possible need to be collected to attain top performances. In this work, we aim to overcome these limitations by leveraging upon the unique and useful characteristics of the extreme learning machine technique, which is based on a collection of randomly chosen hidden units and analytically defined output weights. We find that the suggested approach outperforms state-of-the-art solutions, namely FHMMs and ANNs, on the UK-DALE corpus. Moreover, our solution generalizes better than previous approaches for unseen houses, and avoids a data-hungry training scheme.

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“…Paradiso et al [49] proposed a new electrical load disaggregation system, which utilizes FHMMs and exploits context-based features. The context data consists of the user presence and the power using patterns of devices.…”

Section: Related Workmentioning

confidence: 99%

An Extreme Learning Machine Approach to Effective Energy Disaggregation

Salerno¹,

Rabbeni²

2018

Preprint

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Power disaggregation aims at determining the appliance-by-appliance electricity consumption leveraging upon a single meter only, which measures the entire power demand. Data-driven procedures based on Factorial Hidden Markov Models have been proven remarkable results on energy disaggregation. Nevertheless, those procedures have various weaknesses: there is a scalability problem as the number of devices to observe raises and the algorithmic complexity of the inference step is severe. DNN architectures, such as Convolutional Neural Networks, have demonstrated to be a viable solution to deal with FHMMs shortcomings. Nonetheless, there are two significant limitations: a complicated and time-consuming training system based on back-propagation has to be employed to estimates the neural architecture parameters, and large amounts of training data covering as many operation conditions as possible need to be collected to attain top performances. In this work, we aim to overcome those limitations by leveraging upon the unique and useful characteristics of the extreme learning machine technique, which is based on a collection of randomly chosen hidden units and analytically defined output weights. Experiment evaluation has been conducted using the UK-DALE corpus. We find that the suggested approach achieves similar performances to recently proposed ANN-based methods and outperforms FHMMs. Besides, our solution generalises well to unseen houses.

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Context-Based Energy Disaggregation in Smart Homes

Cited by 43 publications

References 34 publications

On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing

On a Training-Less Solution for Non-Intrusive Appliance Load Monitoring Using Graph Signal Processing

An Extreme Learning Machine Approach to Effective Energy Disaggregation

An Extreme Learning Machine Approach to Effective Energy Disaggregation

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