Research article Topical Sections

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

  • 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.

    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

    Related Papers:

  • 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.


    加载中
    [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.
  • Reader Comments
  • © 2016 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(3345) PDF downloads(1400) Cited by(9)

Article outline

Figures and Tables

Figures(14)  /  Tables(9)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog