A systematic approach in load disaggregation utilizing a multi-stage classification algorithm for consumer electrical appliances classification

doi:10.1007/s11708-017-0497-z

Front. Energy

2019, Vol. 13

Issue (2) : 386-398 https://doi.org/10.1007/s11708-017-0497-z

RESEARCH ARTICLE

A systematic approach in load disaggregation utilizing a multi-stage classification algorithm for consumer electrical appliances classification

Chuan Choong YANG(

), Chit Siang SOH, Vooi Voon YAP

Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, Jalan Kolej, Taman Bandar Baru,? Kampar 31900, Perak, Malaysia

Download: PDF(1473 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

The potential to save energy in existing consumer electrical appliances is very high. One of the ways to achieve energy saving and improve energy use awareness is to recognize the energy consumption of individual electrical appliances. To recognize the energy consumption of consumer electrical appliances, the load disaggregation methodology is utilized. Non-intrusive appliance load monitoring (NIALM) is a load disaggregation methodology that disaggregates the sum of power consumption in a single point into the power consumption of individual electrical appliances. In this study, load disaggregation is performed through voltage and current waveform, known as the V-I trajectory. The classification algorithm performs cropping and image pyramid reduction of the V-I trajectory plot template images before utilizing the principal component analysis (PCA) and the k-nearest neighbor (k-NN) algorithm. The novelty of this paper is to establish a systematic approach of load disaggregation through V-I trajectory-based load signature images by utilizing a multi-stage classification algorithm methodology. The contribution of this paper is in utilizing the “k-value,” the number of closest data points to the nearest neighbor, in the k-NN algorithm to be effective in classification of electrical appliances. The results of the multi-stage classification algorithm implementation have been discussed and the idea on future work has also been proposed.

Keywords load disaggregation voltage-current (V-I) trajectory multi-stage classification algorithm principal component analysis (PCA) k-nearest neighbor (k-NN)

Corresponding Author(s): Chuan Choong YANG

Just Accepted Date: 20 July 2017 Online First Date: 12 September 2017 Issue Date: 04 July 2019

Cite this article:

Chuan Choong YANG,Chit Siang SOH,Vooi Voon YAP. A systematic approach in load disaggregation utilizing a multi-stage classification algorithm for consumer electrical appliances classification[J]. Front. Energy, 2019, 13(2): 386-398.

URL:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-017-0497-z
https://academic.hep.com.cn/fie/EN/Y2019/V13/I2/386

Fig.1 Flow chart of the classification algorithm implementation

Tab.1 Power consumption values and the corresponding channel label for the 10 appliances chosen for the study

Fig.2 Distribution of power values for the 10 electrical appliances

Fig.3 (a) Furnace (Channel Label 10); (b) washer dryer 1 (Channel Label 14); (c) washer dryer 2 (Channel Label 13); (d) microwave (Channel Label 16); (e) bathroom gfi (Channel Label 20); (f) refrigerator (Channel Label 07); (g) electronics (Channel Label 06); (h) lighting 1 (Channel Label 11); (i) lighting 2 (Channel Label 17); (j) lighting 3 (Channel Label 19)

Fig.4 Overview of the multi-stage classification algorithm

Fig.5 V-I trajectory cropped into four quadrants

Fig.6 Examples of cropped and image pyramid—bottom left quadrant

Fig.7 Examples of cropped and image pyramid—top right quadrant

Tab.2 Result of 1st stage classification

Tab.3 Result of 2nd stage classification

Tab.4 Result of 3rd stage classification

1	T Crosbie. Household energy consumption and consumer electronics: the case of television. Energy Policy, 2008, 36(6): 2191–2199 https://doi.org/10.1016/j.enpol.2008.02.010
2	K Roth, B Urban, V Shmakova, B Lim. Residential consumer electronics energy consumption in 2013. In: Proceedings of the ACEEE 2014 Summer Study on Energy Efficiency in Buildings. Washington, 2014, 308–320
3	A G Ruzzelli, C Nicolas, A Schoofs, G M P O’Hare. Real-time recognition and profiling of appliances through a single electricity sensor. In: Proceedings of the 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks. Boston, USA, 2010, 1–9
4	S C Lee, G Y Lin, W R Jih, J Y J Hsu. Appliance recognition and unattended appliance detection for energy conservation. In: Proceeding of the Workshops at the 24 AAAI Conference on Artificial Intelligence. Atlanta, GA, USA, 2010, 37–44
5	M Weiss, A Helfenstein, F Mattern, T Staake. Leveraging smart meter data to recognize home appliances. IEEE Pervasive Computing & Communication, 2012, 22(1): 190–197
6	J Z Moro, L F C Duarte, E C Ferreira, J A S Dias. A home appliance recognition system using the approach of measuring power consumption and power factor on the electrical panel, based on energy meter ICs. Circuits and Systems, 2013, 4(3): 245–251 https://doi.org/10.4236/cs.2013.43033
7	Y W Kim, S B Kong, R K Ko, S K Joo. Electrical event identification technique for monitoring home appliance load using load signatures. In: Proceedings of the 2014 IEEE International Conference on Consumer Electronics (ICCE). Las Vegas, NV, USA, 2014, 296–297
8	M Dong, P C M Meira, W Xu, C Y Chung. Non-intrusive signature extraction for major residential loads. IEEE Transactions on Smart Grid, 2013, 4(3): 1421–1430 https://doi.org/10.1109/TSG.2013.2245926
9	Z Wang, G Zheng. Residential appliances identification and monitoring by a nonintrusive method. IEEE Transactions on Smart Grid, 2012, 3(1): 80–92 https://doi.org/10.1109/TSG.2011.2163950
10	R Streubel, B Yang. Identification of electrical appliances via analysis of power consumption. In: Proceedings of the 47th International Universities Power Engineering Conference (UPEC). Middlesex, United Kingdom, 2012, 1–6
11	M Figueiredo, A de Almeida, B Ribeiro. Home electrical signal disaggregation for non-intrusive load monitoring (NILM) systems. Neurocomput, 2012, 96(3): 66–73 https://doi.org/10.1016/j.neucom.2011.10.037
12	G W Hart. Nonintrusive appliance load monitoring. Proceedings of the IEEE, 1992, 80(12): 1870–1891 https://doi.org/10.1109/5.192069
13	H H Chang, K L Lian, Y C Su, W J Lee. Power-spectrum-based wavelet transform for nonintrusive demand monitoring and load identification. IEEE Transactions on Industry Applications, 2014, 50(3): 2081–2089 https://doi.org/10.1109/TIA.2013.2283318
14	L K Norford, S B Leeb. Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms. Energy and Building, 1996, 24(1): 51–64 https://doi.org/10.1016/0378-7788(95)00958-2
15	H H Chang, H T Yang, C L Lin. Load identification in neural networks for a non-intrusive monitoring of industrial electrical loads. Lecture Notes in Computer Science, 2008, 5236: 664–674 https://doi.org/10.1007/978-3-540-92719-8_60
16	H H Chang, H T Yang. Applying a non-intrusive energy-management system to economic dispatch for a cogeneration system and power utility. Applied Energy, 2009, 86(11): 2335–2343 https://doi.org/10.1016/j.apenergy.2009.03.009
17	H H Chang, K L Chen, Y P Tsai, W J Lee. Anew measurement method for power signatures of nonintrusive demand monitoring and load identification. IEEE Transactions on Industry Applications, 2012, 48(2): 764–771 https://doi.org/10.1109/TIA.2011.2180497
18	H H Chang. Non-intrusive demand monitoring and load identification for energy management systems based on transient feature analyses. Energies, 2012, 5(12): 4569–4589 https://doi.org/10.3390/en5114569
19	H H Chang, C L Lin. A novel information technology of load events detection for the energy management information systems. Information Systems and e-Business Management, 2015, 13(2): 289–308 https://doi.org/10.1007/s10257-014-0261-4
20	C Laughman, K Lee, R Cox, S Shaw, S Leeb, L Norford, P Armstrong. Power signature analysis. IEEE Power & Energy Magazine, 2003, 1(2): 56–63 https://doi.org/10.1109/MPAE.2003.1192027
21	K D Lee. Electrical load information system based on non-intrusive power monitoring. Dissertation for the Doctoral Degree. Boston: Massachusetts Institute of Technology, 2003
22	W K Lee, G S K Fung, H Y Lam, F H Y Chan, M Lucente. Exploration on load signatures. In: Proceedings of the International Conference on Electrical Engineering (ICEE 2004). Sapporo, Japan, 2014, 690–694
23	M Zeifman, K Roth. Nonintrusive appliance load monitoring: review and outlook. IEEE Transactions on Consumer Electronics, 2011, 57(1): 76–84 https://doi.org/10.1109/TCE.2011.5735484
24	A Zoha, A Gluhak, M A Imran, S Rajasegarar. Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey. Sensors (Basel), 2012, 12(12): 16838–16866 https://doi.org/10.3390/s121216838
25	H Y Lam, G S K Fung, W K Lee. A novel method to construct taxonomy of electrical appliances based on load signatures. IEEE Transactions on Consumer Electronics, 2007, 53(2): 653–660 https://doi.org/10.1109/TCE.2007.381742
26	T Hassan, F Javed, N Arshad. An empirical investigation of V-I trajectory based load signatures for non-intrusive load monitoring. IEEE Transactions on Smart Grid, 2014, 5(2): 870–878 https://doi.org/10.1109/TSG.2013.2271282
27	H H Chang. Non-intrusive fault identification of power distribution systems in intelligent buildings based on power-spectrum-based wavelet transform. Energy and Building, 2016, 127: 930–941 https://doi.org/10.1016/j.enbuild.2016.06.050
28	J Z Kolter, M J Johnson. REDD: A public data set for energy disaggregation research. In: Proceedings of the SustKDD Workshop on Data Mining Applications in Sustainability. San Diego, CA, USA, 2011, 1–6
29	I T Jolliffe. Principal Component Analysis. New York: Springer, 2002
30	M Turk, A Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 1991, 3(1): 71–86 https://doi.org/10.1162/jocn.1991.3.1.71
31	Q P He, J Wang. Principal component based k-nearest-neighbor rule for semiconductor process fault detection. American Control Conference, Seattle. WA, USA, 2008, 1606–1611
32	S D Kamath, K K Mahato. Principal component analysis (PCA)-based k-nearest neighbor (k-NN) analysis of colonic mucosal tissue fluorescence spectra. Photomedicine and Laser Surgery, 2009, 27(4): 659–668 https://doi.org/10.1089/pho.2008.2338
33	M N Mansor, S Yaacob, H Muthusamy, S N Basah. PCA-based feature extraction and k-NN algorithm for early jaundice detection. International Journal of Soft Computing and Software Engineering, 2011, 1(1): 25–29
34	P J Burt, E H Adelson. The laplacian pyramid as a compact image code. IEEE Transactions on Communications, 1983, 31(4): 532–540 https://doi.org/10.1109/TCOM.1983.1095851
35	P J Burt. Fast filter transforms for image processing. Computer Graphics and Image Processing, 1981, 16(1): 20–51 https://doi.org/10.1016/0146-664X(81)90092-7
36	E H Adelson, C H Anderson, J R Bergen, P J Burt, J M Ogden. Pyramid methods in image processing. RCA Engineer, 1984, 29(6): 33–41
37	J M Ogden, E H Adelson, J R Bergen, P J Burt. Pyramid-based computer graphics. RCA Engineer, 1985, 30(5): 4–15
38	Y Jin, E Tebekaemi, M Berges, L Soibelman. Robust adaptive event detection in non-intrusive load monitoring for energy aware smart facilities. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing. Prague, Czech Republic, 2011, 4340–4343
39	K Anderson, M Berges, A Ocneanu, D Benitez, J M F Moura. Event detection for non-intrusive load monitoring. In: Proceedings of the 38th Annual Conference on IEEE Industrial Electronics Society (IECON). Montreal, Canada, 2012, 3312–3317
40	F Jazizadeh, B Becerik-Gerber, M Berges, L Soibelman. An unsupervised hierarchical clustering based heuristic algorithm for facilitated training of electricity consumption disaggregation systems. Advanced Engineering Informatics, 2014, 28(4): 311–326 https://doi.org/10.1016/j.aei.2014.09.004
41	C C Yang, C S Soh, V V Yap. A systematic approach to ON-OFF event detection and clustering analysis for non-intrusive appliance load monitoring. Frontiers in Energy, 2015, 9(2): 231–237 https://doi.org/10.1007/s11708-015-0358-6
42	E Arias-Castro, D L Donoho. Does median filtering truly preserve edges better than linear filtering? Annals of Statistics, 2009, 37(3): 1172–1206 https://doi.org/10.1214/08-AOS604
43	C C Yang, C S Soh, V V Yap. Comparative study of event detection methods for non-intrusive appliance load monitoring. Energy Procedia, 2014, 61: 1840–1843 https://doi.org/10.1016/j.egypro.2014.12.225
44	S Giri, P H Lai, M Berges. Novel techniques for on and off states detection of appliances for power estimation innon-intrusive load monitoring. In: Proceedings of the 30th International Symposium on Automation and Robotics in Construction and Mining (ISARC). Montreal, Canada, 2013, 522–530
45	W G Cochran. The x2 test of goodness of fit. Annals of Mathematical Statistics, 1952, 23(3): 315–345 https://doi.org/10.1214/aoms/1177729380
46	F Sultanem. Using appliance signatures for monitoring residential loads at meter panel level. IEEE Transactions on Power Delivery, 1991, 6(4): 1380–1385 https://doi.org/10.1109/61.97667
47	L Smith. A tutorial on principal components analysis. 2015–05–25, available at otago.ac.nz website
48	C Salperwyck, V Lemaire. Learning with few examples: an empirical study on leading classifiers. In: Proceedings of the 2011 International Joint Conference on Neural Networks (IJCNN 2011). California, USA, 2011, 1010–1019
49	S Gupta, M S Reynolds, S N Patel. ElectriSense: single-point sensing using EMI for electrical event detection and classification in the home. In: Proceedings of the 12th ACM International Conference on Ubiquitous Computing. Copenhagen, Denmark, 2010, 139–148

[1]	Zhao WANG, Weisheng WANG, Bo WANG. Regional wind power forecasting model with NWP grid data optimized[J]. Front. Energy, 2017, 11(2): 175-183.

Viewed

Full text

Abstract

Cited

Shared

Discussed