Prospects of Appliance-Level Load Monitoring in Off-the-Shelf Energy Monitors: A Technical Review

Haq, Anwar Ul; Jacobsen, Hans‐Arno

doi:10.3390/en11010189

Cited by 29 publications

(20 citation statements)

References 45 publications

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“…These provide off-the-shelf platforms, normally with the basic functionality ready to be used, but also with some significant drawbacks, especially in terms of sampling rates and flexibility. This trend was already stated in [6], where, after studying different commercially available smart meters and/or energy monitoring, it was concluded that these provide the required computational capacity to cope with advanced techniques, such as NILM. Neurio Technology Inc [7] and Smappee N.V. [8] provide similar energy monitoring solutions, both based on a current clamp, together with a set of utilities and applications intended to display and process the collected information as easily as possible.…”

Section: Data Collectionmentioning

confidence: 71%

“…NILM has been an active topic of research, mainly due to advances in computational intelligence and sensing technology. Although NILM has been around for 30 years now, only recently has the technology made its way into public domain, due to high equipment cost, which hinders the scalability, and a lack of disaggregation accuracy [6]. Future research and development in this area should focus on the solutions to these problems.…”

Section: Guidelines For Future Researchmentioning

confidence: 99%

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NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

et al. 2019

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The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.

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Section: Data Collectionmentioning

confidence: 71%

Section: Guidelines For Future Researchmentioning

confidence: 99%

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

et al. 2019

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“…This new grid will have innovative abilities, such as optimizing energy production and consumption, while taking into account renewable energy as well as electric cars. Smart meters as well as smart monitors (which also measure and calculate power quality parameters) are an essential part of this new system [2].…”

Section: Introductionmentioning

confidence: 99%

Harmonic Loads Classification by Means of Currents’ Physical Components

Beck

Machlev

2019

Energies

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Electric load identification and classification for smart grid environment can improve the power service for both consumers and producers. The main concept of electric load identification and classification is to disaggregate various loads and categorize them. In this paper, a new practical method for electric load identification and classification is presented. The method is based on using a power monitor to analyze a real measured current waveform of a grid-connected device. A set number of features is extracted using the currents’ physical components-based power theory decomposition. Using currents’ physical components ensures a constant number of features, which maintains the signal’s characteristics regardless of the harmonic content. These features are used to train a supervised classifier based on two techniques: artificial neural network and nearest neighbor search. The theory is outlined, and experimental results are shown. This paper demonstrates high accuracy performance in identifying an electric load from a designated database. Furthermore, the results show a definite classification of an untrained operation state of a device to the closest trained operation state, for example, the excitation angle of a dimmer. In a comparative study, the method is shown to outperform other state-of-the-art techniques, which are based on harmonic components.

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“…This is in contrast to the deployment of one sensor per device in the standard intrusive-type of energy monitoring. NILM is worthwhile because it reduces costs, since multiple sensor configurations and installation complexity linked with intrusive load monitoring are avoided [19][20][21][22]. The NILM method is typically preferred because of financial and functional reasons.…”

Section: Introductionmentioning

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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Prospects of Appliance-Level Load Monitoring in Off-the-Shelf Energy Monitors: A Technical Review

Cited by 29 publications

References 45 publications

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

Harmonic Loads Classification by Means of Currents’ Physical Components

An Extreme Learning Machine Approach to Effective Energy Disaggregation

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