An experimental study on electrical signature identification of non-intrusive load monitoring (NILM) systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6594 LNCS, Issue PART 2, 2011, Pages 31-40

  Figueiredo, Marisa B ^a   De Almeida, Ana ^a   Ribeiro, Bernardete ^a  

^a UNIVERSITY OF COIMBRA   (Portugal)

Author keywords

feature extraction and classification; k nearest neighbors; non intrusive load monitoring; steady state signatures; support vector machines

Indexed keywords

ACTIVE AND REACTIVE POWER; COMPUTATIONAL EXPERIMENT; ELECTRIC SIGNAL; ELECTRICAL LOAD; ELECTRICAL SERVICES; ELECTRICAL SIGNAL; ELECTRICAL SIGNATURES; END-USES; EXPERIMENTAL STUDIES; FEATURE EXTRACTION AND CLASSIFICATION; K-NEAREST NEIGHBORS; NON-INTRUSIVE LOAD MONITORING; POWER FACTORS; RESIDENTIAL SECTORS; SIGNAL PARAMETERS; SIGNAL PROCESSING TECHNIQUE; SINGLE POINT; STEADY-STATE SIGNATURES;

ALGORITHMS; ELECTRIC POWER FACTOR; SIGNAL PROCESSING; SUPPORT VECTOR MACHINES;

FEATURE EXTRACTION;

EID: 79955089759     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-20267-4_4     Document Type: Conference Paper

References (15)

1. Hart, G.W.: Nonintrusive appliance load monitoring. Proc. of the IEEE 80, 1870-1891 (1992)
2. Sultanem, F.: Using appliance signatures for monitoring residential loads at meter panel level. IEEE Transactions on Power Delivery 6, 1380-1385 (1991)
3. Leeb, S.B.: A conjoint pattern recognition approach to nonintrusive load monitoring. PhD thesis, Massachusetts Institute of Technology (1993)
4. Cole, A., Albicki, A.: Data extraction for effective non-intrusive identification of residential power loads. In: Instrumentation and Measurement Technology Conf., IMTC 1998. Conf. Proc. IEEE, vol. 2, pp. 812-815 (1998)
5. Cole, A., Albicki, A.: Algorithm for non intrusive identification of residential appliances. In: Proc. of the 1998 IEEE Intl. Symposium on Circuits and Systems, ISCAS 1998, vol. 3, pp. 338-341 (1998)
6. Berges, M., Goldman, E., Matthews, H.S., Soibelman, L.: Learning systems for electric consumption of buildings. In: ASCE Intl. Workshop on Computing in Civil Engineering, Austin, Texas (2009)
7. Bijker, A., Xia, X., Zhang, J.: Active power residential non-intrusive appliance load monitoring system. In: AFRICON 2009, pp. 1-6 (2009)
8. Chang, H.H., Lin, C.L., Lee, J.K.: Load identification in nonintrusive load monitoring using steady-state and turn-on transient energy algorithms. In: 2010 14th Intl. Conf. on Computer Supported Cooperative Work in Design, pp. 27-32 (2010)
9. Figueiredo, M., de Almeida, A., Ribeiro, B., Martins, A.: Extracting features from an electrical signal of a non-intrusive load monitoring system. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osorio, C., Yin, H. (eds.) IDEAL 2010. LNCS, vol. 6283, pp. 210-217. Springer, Heidelberg (2010)
10. ISA Intelligent Sensing Anywhere, S.: Isa intelligent sensing anywhere (2009), http://www.isasensing.com/ [Online; accessed 18-October-2010].
11. Fauvel, M., Chanussot, J., Benediktsson, J.: Evaluation of kernels for multiclass classification of hyperspectral remote sensing data. In: Proc. of IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, vol. 2, pp. 813-816. IEEE, Los Alamitos (2006)
12. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
13. Joachims, T.: Making large-scale svm learning practical. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning. MIT-Press, Cambridge (1999)
14. Crammer, K., Singer, Y., Cristianini, N., Shawe-taylor, J., Williamson, B.: On the algorithmic implementation of multiclass kernel-based vector machines. Journal of Machine Learning Research 2, 265-292 (2001)
15. Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y.: Support vector machine learning for interdependent and structured output spaces. In: ICML 2004: Proc. of the twenty-first Intl. Conf. on Machine learning, p. 104. ACM, New York (2004)