Transient event detection in spectral envelope estimates for nonintrusive load monitoring

1995 ◽  
Vol 10 (3) ◽  
pp. 1200-1210 ◽  
Author(s):  
S.B. Leeb ◽  
S.R. Shaw ◽  
J.L. Kirtley
Keyword(s):  
Event Detection ◽  
Spectral Envelope ◽  
Transient Event ◽  
Load Monitoring

High accuracy event detection for Non-Intrusive Load Monitoring

2017 ◽  
Author(s):  
Mohamed Nait Meziane ◽  
Philippe Ravier ◽  
Guy Lamarque ◽  
Jean-Charles Le Bunetel ◽  
Yves Raingeaud
Keyword(s):  
Event Detection ◽  
High Accuracy ◽  
Load Monitoring

scholarly journals A Multi-Agent NILM Architecture for Event Detection and Load Classification

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4396
Author(s):  
André Eugenio Lazzaretti ◽  
Douglas Paulo Bertrand Renaux ◽  
Carlos Raimundo Erig Lima ◽  
Bruna Machado Mulinari ◽  
Hellen Cristina Ancelmo ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Event Detection ◽  
Classification Accuracy ◽  
Agent Architecture ◽  
Performance Improvements ◽  
Load Monitoring ◽  
Multi Agent ◽  
Improved Performance ◽  
New Algorithms

A multi-agent architecture for a Non-Intrusive Load Monitoring (NILM) solution is presented and evaluated. The underlying rationale for such an architecture is that each agent (load event detection, feature extraction, and classification) outperforms others of the same type in particular scenarios; hence, by combining the expertise of these agents, the system presents an improved performance. Known NILM algorithms, as well as new algorithms, proposed by the authors, were individually evaluated and compared. The proposed architecture considers a NILM system composed of Load Monitoring Modules (LMM) that report to a Center of Operations, required in larger facilities. For the purposed of evaluating and comparing performance, five load event detect agents, five feature extraction agents, and five classification agents were studied so that the best combinations of agents could be implemented in LMMs. To evaluate the proposed system, the COOLL and the LIT-Dataset were used. Performance improvements were detected in all scenarios, with power-ON and power-OFF detection improving up to 13%, while classification accuracy improved up to 9.4%.

scholarly journals Optimization of Survey Strategies for Detecting Slow Radio Transients

2014 ◽  
Vol 31 ◽  
Author(s):  
Jean-Pierre Macquart
Keyword(s):  
Event Detection ◽  
Detection Rate ◽  
Field Of View ◽  
Integration Time ◽  
Transient Event ◽  
Optimal Value ◽  
Drift Scan ◽  
Transient Events ◽  
Radio Transients ◽  
Special Circumstances

AbstractWe investigate the optimal tradeoff between sensitivity and field of view in surveys for slow radio transients using the event detection rate as the survey metric. This tradeoff bears implications for the design of surveys conducted with upcoming widefield radio interferometers, such as the ASKAP VAST survey and the MeerKAT TRAPUM survey. We investigate (i) a survey in which the events are distributed homogeneously throughout a volume centred on the Earth, (ii) a survey in which the events are homogeneously distributed, but are only detectable beyond a certain minimum distance, and (iii) a survey in which all the events occur at an identical distance, as is appropriate for a targetted survey of a particular field which subtends Npoint telescope pointings. For a survey of fixed duration, Tobs, we determine the optimal tradeoff between number of telescope pointings, N, and integration time per field. We consider a population in which the event luminosity distribution follows a power law with index − α, and tslew is the slewing time between fields or, for a drift scan, the time taken for the telescope drift by one beamwidth. Several orders of magnitude improvement in detection rate is possible by optimization of the survey parameters. The optimal value of N for case (i) is Nmax ~ Tobs/4tslew, while for case (iii) we find Nmax = (Lmax/L0)2[(3 − α)/2]2/(α − 1), where Lmax is the maximum luminosity of a transient event and L0 is the minimum luminosity event detectable in an integration of duration Tobs. (The instance Nmax > Npoint in (iii) implies re-observation of fields over the survey area, except when the duration of transient events exceeds that between re-observations of the same field, where Nmax = Npoint applies instead.) We consider the balance in survey optimization between telescope field of view, Ω, and sensitivity, characterised by the minimum detectable flux density, S0. For homogeneously distributed events (i), the detection rate scales as NΩS−3/20, while for targetted events (iii) it scales as NΩS1 − α0. However, if the targetted survey is optimised for N the event detection rate scales instead as ΩS−20. This analysis bears implications for the assessment of telescope designs: the quantity ΩS−20 is often used as the metric of telescope performance in the SKA transients literature, but only under special circumstances is it the metric that optimises the event detection rate.

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