Non-intrusive load monitoring based on low frequency active power measurements

Chinthaka Dinesh; Pramuditha Perera; Roshan Indika Godaliyadda; Mervyn Parakrama B. Ekanayake; Janaka Ekanayake; Chinthaka Dinesh; Pramuditha Perera; Roshan Indika Godaliyadda; Mervyn Parakrama B. Ekanayake; Janaka Ekanayake

doi:10.3934/energy.2016.3.414

AIMS Energy

2016, Volume 4, Issue 3: 414-443. doi: 10.3934/energy.2016.3.414

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Non-intrusive load monitoring based on low frequency active power measurements

1.
Department of Electrical and Electronic Engineering, Univesity of Peradeniya, Sri Lanka
2.
Department of Electrical and Computer Engineering, Rutgers University, USA
3.
Cardiff School of Engineering, Cardiff University, UK

Received: 12 December 2015 Accepted: 21 March 2016 Published: 25 March 2016

A Non-Intrusive Load Monitoring (NILM) method for residential appliances based on active power signal is presented. This method works effectively with a single active power measurement taken at a low sampling rate (1 s). The proposed method utilizes the Karhunen Loéve (KL) expansion to decompose windows of active power signals into subspace components in order to construct a unique set of features, referred to as signatures, from individual and aggregated active power signals. Similar signal windows were clustered in to one group prior to feature extraction. The clustering was performed using a modified mean shift algorithm. After the feature extraction, energy levels of signal windows and power levels of subspace components were utilized to reduce the number of possible appliance combinations and their energy level combinations. Then, the turned on appliance combination and the energy contribution from individual appliances were determined through the Maximum a Posteriori (MAP) estimation. Finally, the proposed method was modified to adaptively accommodate the usage patterns of appliances at each residence. The proposed NILM method was validated using data from two public databases: tracebase and reference energy disaggregation data set (REDD). The presented results demonstrate the ability of the proposed method to accurately identify and disaggregate individual energy contributions of turned on appliance combinations in real households. Furthermore, the results emphasise the importance of clustering and the integration of the usage behaviour pattern in the proposed NILM method for real households.
- Non-intrusive load monitoring (NILM),
- appliance identification,
- energy disaggregation,
- smart grid,
- smart meter,
- uncorrelated spectral information,
- subspace component,
- window clustering,
- appliance usage pattern,
- priori probability
Citation: Chinthaka Dinesh, Pramuditha Perera, Roshan Indika Godaliyadda, Mervyn Parakrama B. Ekanayake, Janaka Ekanayake. Non-intrusive load monitoring based on low frequency active power measurements[J]. AIMS Energy, 2016, 4(3): 414-443. doi: 10.3934/energy.2016.3.414

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Abstract

A Non-Intrusive Load Monitoring (NILM) method for residential appliances based on active power signal is presented. This method works effectively with a single active power measurement taken at a low sampling rate (1 s). The proposed method utilizes the Karhunen Loéve (KL) expansion to decompose windows of active power signals into subspace components in order to construct a unique set of features, referred to as signatures, from individual and aggregated active power signals. Similar signal windows were clustered in to one group prior to feature extraction. The clustering was performed using a modified mean shift algorithm. After the feature extraction, energy levels of signal windows and power levels of subspace components were utilized to reduce the number of possible appliance combinations and their energy level combinations. Then, the turned on appliance combination and the energy contribution from individual appliances were determined through the Maximum a Posteriori (MAP) estimation. Finally, the proposed method was modified to adaptively accommodate the usage patterns of appliances at each residence. The proposed NILM method was validated using data from two public databases: tracebase and reference energy disaggregation data set (REDD). The presented results demonstrate the ability of the proposed method to accurately identify and disaggregate individual energy contributions of turned on appliance combinations in real households. Furthermore, the results emphasise the importance of clustering and the integration of the usage behaviour pattern in the proposed NILM method for real households.

References

[1]	Hart GW (1992) Nonintrusive appliance load monitoring. Proc IEEE 80: 1870-1891. doi: 10.1109/5.192069
[2]	Erol-Kantarci M, Mouftah HT (2011) Wireless Sensor Networks for Cost-Efficient Residential Energy Management in the Smart Grid. IEEE Trans Smart grid 2: 314-325. doi: 10.1109/TSG.2011.2114678
[3]	Yu-Hsiu Lin, Men-Shen Tsai (2014) Development of an Improved Time Frequency AnalysisBased Nonintrusive Load Monitor for Load Demand Identification. IEEE Trans Instrumentation and Measurement 63: 1470-1483. doi: 10.1109/TIM.2013.2289700
[4]	Dong M, Meira MCM, Xu W, et al (2012) An Event Window Based Load Monitoring Technique for Smart Meters. IEEE Trans Smart Grid 3: 782-796.
[5]	Figueiredo MB, Almeida AD, Ribeiro B (2011) An Experimental Study on Electrical Signature Identification of Non-Intrusive Load Monitoring System. Proc 10th Int Conf Adaptive and Natural Computer Algorithm (ICANNGA’11) 747-758.
[6]	Liang J, Ng S, Kendall G, et al (2010) Load Signature Study-Part I: Basic Concept, Structure, and Methodology. IEEE Trans Power Del 25: 551-560. doi: 10.1109/TPWRD.2009.2033799
[7]	Liang J, Ng S, Kendall G, et al (2010) Load Signature Study-Part II: Disaggregation Framework, Simulation and Applications, Structure, and Methodology. IEEE Trans Power Del 25: 561-569. doi: 10.1109/TPWRD.2009.2033800
[8]	Li J, Ng SKK, Kendall G, et al (2012) Power Decomposition Based on SVM Regression. 2012 Proc IEEE Int Conf Modelling, Identification and Control : 1195-1199.
[9]	Lam H, Fung G, Lee W (2007) A Novel Method to Construct Taxonomy Electrical Appliances Based on Load Signature. IEEE Trans Consumer Electronics 53: 653-660. doi: 10.1109/TCE.2007.381742
[10]	Wang Z, Zheng G (2012) Residential Appliances Identification and Monitoring by a Nonintrusive Method. IEEE Trans Smart Grid 3: 80-92. doi: 10.1109/TSG.2011.2163950
[11]	Zeifman M, Roth K (2011) Nonintrusive appliance load monitoring: Review and outlook. IEEE Trans Consumer Electronics 57: 76-84. doi: 10.1109/TCE.2011.5735484
[12]	Giusti A, Salani M, Gianni A, et al (2014) Restricted Neighbourhood Communication Improves Decentralized Demand-Side Load Management. IEEE Trans Smart grid 5: 92-101. doi: 10.1109/TSG.2013.2267396
[13]	Parson O, Ghosh S, Weal M, et al (2012) Non-intrusive load monitoring using prior models of general appliance types. Proc IEEE Int Conf Artificial Intelligence(AAAI-12) 356-362.
[14]	Zoha A, Glihak A, Imran MU, et al (2012) Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey. Sensors 12: 16838-16866. doi: 10.3390/s121216838
[15]	Dinesh C, Nettasinhe BW, Godaliyadda RI, et al (2015) Residential Appliance Identification Based on Spectral Information of Low Frequency Smart Meter Measurements. IEEE Trans Smart Grids (In press).
[16]	Reinhardt A, Baumann P, Burgstahler D, et al (2012) On the Accuracy of Appliance Identification Based on Distributed Load Metering data. Proc IEEE Int Conf Sustainable Internet and ICT for Sustainability (SustainlT’2012) : 1-9.
[17]	Kolter J, Johnson M (2011) REDD: A public data set for energy disaggregation research. in Workshop on Data Mining Applications in Sustainability (SIGKDD) 1-6.
[18]	Maccone, Claudio (1994) Telecommunications, KLT and relativity. IPI Press.
[19]	Cheng TZ (1995) Mean Shift, Mode Seeking, and Clustering. IEEE Trans Pattern Anal Mach Intell 17: 790-799. doi: 10.1109/34.400568
[20]	Georgescu B, Shimshoni I, Meer P (2003) Mean Shift Based Clustering in High Dimensions: A Texture Classification Example. 2002 Proc IEEE Int Conf Computer Vision 1: 456-463.
[21]	Zoha A (2002) Statistical inference. Duxbury Pacific Grove, CA 2.
[22]	Olson DL , Delen D (2008) Advanced Data Mining Techniques. Springer, 1.

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