Event detection for Non Intrusive load monitoring

Anderson, Ken; Bergés, Mario; Ocneanu, Adrian; Benítez, Diego S.; Moura, J.M.F.

doi:10.1109/iecon.2012.6389367

Cited by 208 publications

(91 citation statements)

References 9 publications

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“…False positive rate (Anderson, Bergés, et al, 2012;Batra, Kelly, et al, 2014) is the proportion of false positives against the actual negative results.…”

Section: Tpmentioning

confidence: 99%

“…-P Precision (Anderson, Bergés, Ocneanu, Benitez, & Moura, 2012;Batra, Kelly, et al, 2014;Beckel et al, 2014;Berges, 2010) (also called positive predictive value-PPV), is the proportion of relevant instances that were reported as being relevant against all the instances that were reported as relevant.…”

Section: Tpmentioning

confidence: 99%

“…False positive percentage (Anderson, Bergés, et al, 2012) is the percentage of the ratio between false positives and actual true results. Note that since the number of FP can be larger that the number of real events, FPP can be larger than 100%.…”

Section: Oa =mentioning

confidence: 99%

“…The F β -measure (Anderson, Bergés, et al, 2012;Batra, Kelly, et al, 2014;Beckel et al, 2014;Berges, 2010) trades-off precision and recall. Mathematically, it is the harmonic mean between the two metrics.…”

Section: Tpmentioning

confidence: 99%

“…Ndis Ntrue TPP True positive percentage (Anderson, Bergés, et al, 2012) is the percentage of the ratio between true positives and actual true results. TPP is equivalent to recall.…”

Section: Oa =mentioning

confidence: 99%

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Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review

Pereira¹,

Nunes

2018

WIREs Data Min & Knowl

139

103

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Non-intrusive load monitoring (also known as NILM or energy disaggregation) is the process of estimating the energy consumption of individual appliances from electric power measurements taken at a limited number of locations in the electric distribution of a building. This approach reduces sensing infrastructure costs by relying on machine learning techniques to monitor electric loads. However, the ability to evaluate and benchmark the proposed approaches across different datasets is key for enabling the generalization of research findings and consequently contributes to the large-scale adoption of this technology. Still, only recently researchers have focused on creating and standardizing the existing datasets in order to deliver a single interface to run NILM evaluations. Furthermore, there is still no consensus regarding, which performance metrics should be used to measure and report the performance of NILM systems and their underlying algorithms. This paper provides a review of the main datasets, metrics, and tools for evaluating the performance of NILM systems and technologies. Specifically, we review three main topics: (a) publicly available datasets, (b) performance metrics, and (c) frameworks and toolkits. The review suggests future research directions in NILM systems and technologies, including cross-datasets, performance metrics for evaluation and generalizable frameworks for benchmarking NILM technology.

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“…False positive rate (Anderson, Bergés, et al, 2012;Batra, Kelly, et al, 2014) is the proportion of false positives against the actual negative results.…”

Section: Tpmentioning

confidence: 99%

Section: Tpmentioning

confidence: 99%

Section: Oa =mentioning

confidence: 99%

Section: Tpmentioning

confidence: 99%

“…Ndis Ntrue TPP True positive percentage (Anderson, Bergés, et al, 2012) is the percentage of the ratio between true positives and actual true results. TPP is equivalent to recall.…”

Section: Oa =mentioning

confidence: 99%

See 3 more Smart Citations

Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review

Pereira¹,

Nunes

2018

WIREs Data Min & Knowl

139

103

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Achieving differential privacy against non‐intrusive load monitoring in smart grid: A fog computing approach

Cao

Liu

et al. 2018

Concurrency and Computation

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Fog computing, a non-trivial extension of cloud computing to the edge of the network, has great advantage in providing services with a lower latency. In smart grid, the application of fog computing can greatly facilitate the collection of consumer's fine-grained energy consumption data, which can then be used to draw the load curve and develop a plan or model for power generation. However, such data may also reveal customer's daily activities. Non-intrusive load monitoring (NILM) can monitor an electrical circuit that powers a number of appliances switching on and off independently. If an adversary analyzes the meter readings together with the data measured by an NILM device, the customer's privacy will be disclosed. In this paper, we propose an effective privacy-preserving scheme for electric load monitoring, which can guarantee differential privacy of data disclosure in smart grid. In the proposed scheme, an energy consumption behavior model based on Factorial Hidden Markov Model (FHMM) is established. In addition, noise is added to the behavior parameter, which is different from the traditional methods that usually add noise to the energy consumption data. The analysis shows that the proposed scheme can get a better trade-off between utility and privacy compared with other popular methods.

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Appliance Identification Through Nonintrusive Load Monitoring in Residences

Gogos

Georgiou

2019

Computational Intelligence and Optimization Methods for Control Engineering

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Event detection for Non Intrusive load monitoring

Cited by 208 publications

References 9 publications

Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review

Performance evaluation in non‐intrusive load monitoring: Datasets, metrics, and tools—A review

Achieving differential privacy against non‐intrusive load monitoring in smart grid: A fog computing approach

Appliance Identification Through Nonintrusive Load Monitoring in Residences

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