Using neural networks for non-intrusive monitoring of industrial electrical loads

Roos, J.G.; Lane, I.E.; Botha, Elizabeth C.; Hancke, Gerhard P.

doi:10.1109/imtc.1994.351862

Cited by 72 publications

(42 citation statements)

References 10 publications

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“…Early research focused on disaggregating simple resistive appliances that have a finite number of operating states, which are typical for the residential sector (e.g., Hart, 1992;Sultanem, 1991;Marceau and Zmeureanu, 2000;Leeb et al, 1995). Some effort (e.g., Roos et al, 1994;Lee et al, 2005) has also been invested in studying electric appliances in the industrial and commercial sector, which commonly have more complicated operating principles (e.g., variable power devices such as motors).…”

Section: Appendix B Description Of Published Algorithmsmentioning

confidence: 99%

“…A number of studies extended the simple linear appliance model adopted by Hart (1992) to use other directly sampled quantities such as jump in current DI (e.g., Sultanem, 1991) or voltage distortion (Roos et al, 1994) to augment (and hence increase the resolving power) of the (DO, DQ) space. All these approaches require appreciable amounts (several days) of edge data to be processed off-line.…”

Section: Appendix B Description Of Published Algorithmsmentioning

confidence: 99%

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Is disaggregation the holy grail of energy efficiency? The case of electricity

Gupta²,

et al. 2013

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This paper aims to address two timely energy problems. First, significant low-cost energy reductions can be made in the residential and commercial sectors, but these savings have not been achievable to date. Second, billions of dollars are being spent to install smart meters, yet the energy saving and financial benefits of this infrastructure -without careful consideration of the human element -will not reach its full potential. We believe that we can address these problems by strategically marrying them, using disaggregation. Disaggregation refers to a set of statistical approaches for extracting end-use and/or appliance level data from an aggregate, or wholebuilding, energy signal. In this paper, we explain how appliance level data affords numerous benefits, and why using the algorithms in conjunction with smart meters is the most cost-effective and scalable solution for getting this data. We review disaggregation algorithms and their requirements, and evaluate the extent to which smart meters can meet those requirements. Research, technology, and policy recommendations are also outlined.

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Section: Appendix B Description Of Published Algorithmsmentioning

confidence: 99%

Section: Appendix B Description Of Published Algorithmsmentioning

confidence: 99%

Is disaggregation the holy grail of energy efficiency? The case of electricity

Gupta²,

et al. 2013

View full text Add to dashboard Cite

show abstract

“…Also, the power-based methods may not recognize the appliances where the consumed powers change non-discretely [6]. Roos et al [7] analyzed the details of load under steady-state operation for identification. This analysis, however, puts computers in a computationally complicated situation for accurate power signature data.…”

Section: Introductionmentioning

confidence: 99%

Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

Chang

Linh

2017

Energies

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This paper proposes statistical feature extraction methods combined with artificial intelligence (AI) approaches for fault locations in non-intrusive single-line-to-ground fault (SLGF) detection of low voltage distribution systems. The input features of the AI algorithms are extracted using statistical moment transformation for reducing the dimensions of the power signature inputs measured by using non-intrusive fault monitoring (NIFM) techniques. The data required to develop the network are generated by simulating SLGF using the Electromagnetic Transient Program (EMTP) in a test system. To enhance the identification accuracy, these features after normalization are given to AI algorithms for presenting and evaluating in this paper. Different AI techniques are then utilized to compare which identification algorithms are suitable to diagnose the SLGF for various power signatures in a NIFM system. The simulation results show that the proposed method is effective and can identify the fault locations by using non-intrusive monitoring techniques for low voltage distribution systems.

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“…Since then, a number of studies have extended its simple linear model to use other directly sampled quantities to augment and increase the resolving power of the ∆P -∆Q space [38] [43]. This approaches couldn't distinguish appliances that draw similar power and similar operational principles, such as an iron and a hair dryer.…”

Section: Related Workmentioning

confidence: 99%

Non Intrusive Load Monitoring (NILM): A State of the Art

Herrero

Murciego

Barriuso

et al. 2018

Advances in Intelligent Systems and Computing

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Abstract. The recent increase in smart meters installations in households and small bussiness by electric companies has led to interest in monitoring load techniques in order to provide better quality service and get useful information about appliance usage and user consumption behavior. This works summarizes the current state of the art in Non Intrusive Load Monitoring from its beginning, describes the main process followed in the literature to perform this technique and shows current methods and techniques followed nowadays. The possible application of this techniques in the context of ambient intelligence, energy efficiency, occupancy detection are described. This work also points the current challenges in the field and the future lines of research in this broad topic.

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Using neural networks for non-intrusive monitoring of industrial electrical loads

Cited by 72 publications

References 10 publications

Is disaggregation the holy grail of energy efficiency? The case of electricity

Is disaggregation the holy grail of energy efficiency? The case of electricity

Statistical Feature Extraction for Fault Locations in Nonintrusive Fault Detection of Low Voltage Distribution Systems

Non Intrusive Load Monitoring (NILM): A State of the Art

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