Data extraction for effective non-intrusive identification of residential power loads

Cole, Andrew; Albicki, A.

doi:10.1109/imtc.1998.676838

Cited by 70 publications

(29 citation statements)

References 2 publications

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“…These sources can include total yearly consumption with mean daily consumption profiles Sidler, 1996; or just the yearly electricity consumption (Mansouri et al, 1996;Nutek, 1994;SLY, 1995). Also typical power levels of household appliances are available, partly as they are crucial for some of the popular non-intrusive load monitoring methods (Cole and Albicki, 1998;Hart, 1992;Pihala, 1998;Rissanen, 1998;Sultanem, 1991;Wood and Newborough, 2003). The standby consumption levels are available at multiple sources (Meier et al, 2004;Rosen and Meier, 2000;Ross and Meier, 2002;Sidler, 1996;.…”

Section: Mathematical Model For Household Electricity Consumptionmentioning

confidence: 99%

A model for generating household electricity load profiles

Paatero¹,

Lund²

2006

Int. J. Energy Res.

429

196

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SUMMARYElectricity consumption data profiles that include details on the consumption can be generated with a bottom-up load models. In these models the load is constructed from elementary load components that can be households or even their individual appliances. In this work a simplified bottom-up model is presented. The model can be used to generate realistic domestic electricity consumption data on an hourly basis from a few up to thousands of households. The model uses input data that is available in public reports and statistics. Two measured data sets from block houses are also applied for statistical analysis, model training, and verification. Our analysis shows that the generated load profiles correlate well with real data. Furthermore, three case studies with generated load data demonstrate some opportunities for appliance level demand side management (DSM). With a mild DSM scheme using cold loads, the daily peak loads can be reduced 7.2% in average. With more severe DSM schemes the peak load at the yearly peak day can be completely leveled with 42% peak reduction and sudden three hour loss of load can be compensated with 61% mean load reduction.

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Section: Mathematical Model For Household Electricity Consumptionmentioning

confidence: 99%

A model for generating household electricity load profiles

Paatero¹,

Lund²

2006

Int. J. Energy Res.

429

196

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“…In Hart [1], the loads were identified based on the real and reactive powers. Cole and Albicki [5] also evaluated data extraction for NILM using power-based methods. Nevertheless, this has the limitation that various loads have the same power consumption.…”

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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“…This technique, however, is expensive for the detection of transients. In addition, the detection of transient behavior can be obscured by the simultaneous transient of other loads [6]. Cole [6], [7] examined a data extraction method and a steadystate load identification algorithm for NILM.…”

Section: Introductionmentioning

confidence: 99%

Load identification in nonintrusive load monitoring using steady-state and turn-on transient energy algorithms

Chang

Lin

Lee

2010

The 2010 14th International Conference on Computer Supported Cooperative Work in Design

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ҏ Non-intrusive load monitoring (NILM) techniques are based on the analysis of load energy signatures. With characterizing associated transient energy signature, the reliability and accuracy of recognition results can be accurately understood or ascertained. In this study, the computer supported cooperative work techniques (CSCW), artificial neural networks (ANN), in combination with turn-on transient energy analysis, are used to identify loads and to improve recognition accuracy and computational speed of NILM results. The experimental results indicated that the incorporation of turn-on transient energy signature analysis into NILM revealed more information than traditional NILM methods, and the resulting recognition accuracy and computational speed were improved. In addition, in combination with computer supported cooperative work in electromagnetic transient program (EMTP) simulation, calculations of turn-on transient energy facilitated load identification that had significant effect on NILM results.

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Data extraction for effective non-intrusive identification of residential power loads

Cited by 70 publications

References 2 publications

A model for generating household electricity load profiles

A model for generating household electricity load profiles

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

Load identification in nonintrusive load monitoring using steady-state and turn-on transient energy algorithms

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