Implementation of a robust real-time non-intrusive load monitoring solution

Welikala, Shirantha; Thelasingha, Neelanga; Akram, Muhammed; Ekanayake, Parakrama; Godaliyadda, G. M. R. I.; Ekanayake, Janaka

doi:10.1016/j.apenergy.2019.01.167

Cited by 40 publications

(18 citation statements)

References 18 publications

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“…The most employed feature is S, exclusively used in [26,[49][50][51]; P and Q were employed in [52,53]. In a recent work [54] P and V RMS measurements were used, sampled at 1 Hz, obtaining a high level of accuracy, even with varying supply voltages.…”

Section: Feature Setsmentioning

confidence: 99%

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: Feature Setsmentioning

confidence: 99%

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

et al. 2019

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“…hardwired smoke detector, telephone sets). A similar categorisation was introduced by Hamid et al [38]. In this study, all devices were analysed as two-state appliances (on-off-type (i) or low-high consumption-a subtype of type (ii)).…”

Section: Selection and Preparation Of Input And Output Variables Of Mmentioning

confidence: 99%

“…On the one hand, the use of a ten-minute period eliminates (to an extent) the problem of real power consumption fluctuations (described by Welikala et al [38]) through the process of averaging. As a result, it is easier to identify the activity of an appliance.…”

Section: Selection and Preparation Of Input And Output Variables Of Mmentioning

confidence: 99%

Modelling the Disaggregated Demand for Electricity in Residential Buildings Using Artificial Neural Networks (Deep Learning Approach)

Jasiński

2020

Energies

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The paper addresses the issue of modelling the demand for electricity in residential buildings with the use of artificial neural networks (ANNs). Real data for six houses in Switzerland fitted with measurement meters was used in the research. Their original frequency of 1 Hz (one-second readings) was re-sampled to a frequency of 1/600 Hz, which corresponds to a period of ten minutes. Out-of-sample forecasts verified the ability of ANNs to disaggregate electricity usage for specific applications (electricity receivers). Four categories of electricity consumption were distinguished: (i) fridge, (ii) washing machine, (iii) personal computer, and (iv) freezer. Both standard ANNs with multilayer perceptron architecture and newer types of networks based on deep learning were used. The simulations included over 10,000 ANNs with different architecture (number of neurons and structure of their connections), type and number of input variables, formulas of activation functions, training algorithms, and other parameters. The research confirmed the possibility of using ANNs to model the disaggregation of electricity consumption based on low frequency data, and suggested ways to build highly optimised models.

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“…The model accurately identified the energy consumption of refrigerators, microwaves, and dishwashers with a low mean square error (MSE) and mean absolute error. A NIM algorithm based on Karhunen-Loeve expansion was proposed to disaggregate household appliances using voltage-specific appliance signatures to ensure the accuracy of disaggregation in the case of large voltage fluctuations [19]. The disaggregation algorithm yielded accurate results with an average accuracy of over 92.5%, even under severe voltage fluctuations.…”

Section: Introductionmentioning

confidence: 99%

A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest

Ling¹,

Tao²,

Zheng³

et al. 2021

Buildings

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Load monitoring can help users learn end-use energy consumption so that specific energy-saving actions can be taken to reduce the energy consumption of buildings. Nonintrusive monitoring (NIM) is preferred because of its low cost and nondisturbance of occupied space. In this study, a NIM method based on random forest was proposed to determine the energy consumption of building subsystems from the building-level energy consumption: the heating, ventilation and air conditioning system; lighting system; plug-in system; and elevator system. Three feature selection methods were used and compared to achieve accurate NIM based on weather parameters, wavelet analysis, and principal component analysis. The implementation of the proposed method in an office building showed that it can obtain the subloads accurately, with root-mean-square errors of less than 46.4 kW and mean relative errors of less than 12.7%. The method based on weather parameters can provide the most accurate results. The proposed method can help improve the energy efficiency of building service systems during the operation or renovation stage.

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Implementation of a robust real-time non-intrusive load monitoring solution

Cited by 40 publications

References 18 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

Modelling the Disaggregated Demand for Electricity in Residential Buildings Using Artificial Neural Networks (Deep Learning Approach)

A Nonintrusive Load Monitoring Method for Office Buildings Based on Random Forest

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