Appliance electrical consumption modelling at scale using smart meter data

Murray, David M.; Stanković, Lina; Espinoza‐Orias, Namy

doi:10.1016/j.jclepro.2018.03.163

Cited by 13 publications

(10 citation statements)

References 33 publications

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“…The ability to obtain appliance-level load measurements from smart meter aggregate data, using purely computational software methods with improved accuracy (see [14,31,32,11,10] for recent surveys of methods) has also ignited broader applications beyond energy feedback such as device scheduling, recommendation engine, demand response capacity estimation, itemized bills [33,6], appliance mining [4], consumer studies [5], etc. (see [7,8,9] for surveys of NILM applications), that either relied on submetered appliance-level power traces or appliance models, which do not represent actual usage patterns.…”

Section: Nilmmentioning

confidence: 99%

“…• Disaggregation techniques: We use publicly available implementations, NILMTK 5 (for CO and FHMM), LBM 6 , and SSHMM 7 to get disaggregation results. Table 5 shows the division of training and testing data used in the techniques.…”

Section: Experimental Settingsmentioning

confidence: 99%

“…On the other hand, Non-intrusive load monitoring (NILM) estimates the individual consumption of an appliance within a building from the aggregate meter reading obtained from a smart meter, measuring total household electricity consumption at each sampling point; effectively eliminating the need for submetering. The effectiveness of NILM has been demonstrated in providing appliance [4,5] and activity-based feedback [6] to consumers, utilities, and policy makers (see [7,8,9,10,11] for recent reviews).…”

Section: Introductionmentioning

confidence: 99%

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Can non-intrusive load monitoring be used for identifying an appliance’s anomalous behaviour?

2019

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Identification of faulty appliance behaviour in real time can signal energy wastage and the need for appliance servicing or replacement leading to energy savings.The problem of appliance fault or anomaly detection has been tackled vastly in relation to submetering, which is not scalable since it requires separate meters for each appliance. At the same time, for applications such as energy feedback, Non-intrusive load monitoring (NILM) has been recognised as a scalable and practical alternative to submetering. However, the usability of NILM for anomaly detection has not yet been investigated. Since the goal of NILM is to provide energy consumption estimate, it is unclear if the signal fidelity of appliance signatures generated by state-of-the-art NILM is sufficient to enable accurate appliance fault detection. In this paper, we attempt to determine whether appliance signatures detected by NILM can be used directly for anomaly detection. This is carried out by proposing an anomaly detection algorithm which performs well for submetering data and evaluate its ability to identify the same faulty behaviour of appliances but with NILM-generated appliance power traces. Our results on a dataset of six residential homes using four state-of-the-art NILM algorithms show that, on average, NILM traces are not as robust to identification of faulty behaviour as compared to using submetered data. We discuss in detail observations pertaining to the reconstructed appliance signatures following NILM and their fidelity with respect to noise-free

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Section: Nilmmentioning

confidence: 99%

Section: Experimental Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Can non-intrusive load monitoring be used for identifying an appliance’s anomalous behaviour?

2019

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“…The initial interest of the scientific community to formulate and describe mathematically the NILM problem using machine learning tools—which is the main topic of interest in the review papers that are currently available—has now shifted into a more practical approach towards NILM. Currently, we are in the mature NILM period where there is an attempt for NILM to be applied in real-life application scenarios [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Thus, the complexity of the algorithms, transferability, reliability, practicality, and, in general, trustworthiness are the main issues of interest.…”

Section: Introductionmentioning

confidence: 99%

Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring

Kaselimi

Protopapadakis

Voulodimos

et al. 2022

Sensors

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Non-intrusive load monitoring (NILM) is the task of disaggregating the total power consumption into its individual sub-components. Over the years, signal processing and machine learning algorithms have been combined to achieve this. Many publications and extensive research works are performed on energy disaggregation or NILM for the state-of-the-art methods to reach the desired performance. The initial interest of the scientific community to formulate and describe mathematically the NILM problem using machine learning tools has now shifted into a more practical NILM. Currently, we are in the mature NILM period where there is an attempt for NILM to be applied in real-life application scenarios. Thus, the complexity of the algorithms, transferability, reliability, practicality, and, in general, trustworthiness are the main issues of interest. This review narrows the gap between the early immature NILM era and the mature one. In particular, the paper provides a comprehensive literature review of the NILM methods for residential appliances only. The paper analyzes, summarizes, and presents the outcomes of a large number of recently published scholarly articles. Furthermore, the paper discusses the highlights of these methods and introduces the research dilemmas that should be taken into consideration by researchers to apply NILM methods. Finally, we show the need for transferring the traditional disaggregation models into a practical and trustworthy framework.

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“…NILM has the potential to provide low-cost, efficient and fine-grained energy feedback that can potentially reduce domestic electricity consumption by 0.7%-4.5% compared to only aggregate consumption feedback [8]. However, the applications of NILM go beyond supporting energy-efficient behaviour [9,10], as NILM has already been shown to support national surveys on energy intensity of domestic activities [11], scalable appliance modelling [12], accurate estimation of the residential consumption phase of food life-cycle assessments [13], house maintenance and retrofit [14], and detection of anomalous appliances [15].…”

Section: Introductionmentioning

confidence: 99%

Non-intrusive load disaggregation solutions for very low-rate smart meter data

Zhao

Stanković

2020

Applied Energy

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With the active large-scale roll-out of smart metering worldwide, details about the type of smart meter data that will be available for analysis are emerging.Consequently, focus has steadily been shifting from analysis of high-rate power readings (usually in kHz to MHz) to low-rate power readings (sampled at 1 to 60 sec) and very low-rate meter readings of the order of 15-60 minutes. This has triggered renewed research into practical non-intrusive load disaggregation of low-to very-low granularity meter readings to address challenges not addressed by existing disaggregation approaches, namely, indistinct appliance ON/OFF transitions, increased likelihood of overlapping appliance usage within a sample and noise due to unknown appliances. In this paper, focusing on smart meter readings at hourly resolution, three load disaggregation solutions are proposed based on: (i) optimisation (minimisation of error between aggregate and disaggregated loads), (ii) graph signal processing and (iii) convolutional neural network. These are benchmarked with state-of-the-art approaches, based on factorial hidden Markov model and combinatorial optimisation implemented in the NILMTK toolbox, and discriminative disaggregation sparse coding. The hourly electricity profile data is obtained from real-world active power readings from the REFIT dataset 1 over a period of longer than one year. All proposed

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Appliance electrical consumption modelling at scale using smart meter data

Cited by 13 publications

References 33 publications

Can non-intrusive load monitoring be used for identifying an appliance’s anomalous behaviour?

Can non-intrusive load monitoring be used for identifying an appliance’s anomalous behaviour?

Towards Trustworthy Energy Disaggregation: A Review of Challenges, Methods, and Perspectives for Non-Intrusive Load Monitoring

Non-intrusive load disaggregation solutions for very low-rate smart meter data

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