Abstract
Electrical load disambiguation for end-use recognition in the residential sector has become an area of study of its own right. Several works have shown that individual loads can be detected (and separated) from sampling of the power at a single point (e.g. the electrical service entrance for the house) using a non-intrusive load monitoring (NILM) approach. This work presents the development of an algorithm for electrical feature extraction and pattern recognition, capable of determining the individual consumption of each device from the aggregate electric signal of the home. Namely, the idea consists of analyzing the electrical signal and identifying the unique patterns that occur whenever a device is turned on or off by applying signal processing techniques. We further describe our technique for distinguishing loads by matching different signal parameters (step-changes in active and reactive powers and power factor) to known patterns. Computational experiments show the effectiveness of the proposed approach.
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References
Hart, G.W.: Nonintrusive appliance load monitoring. Proc. of the IEEE 80, 1870–1891 (1992)
Sultanem, F.: Using appliance signatures for monitoring residential loads at meter panel level. IEEE Transactions on Power Delivery 6, 1380–1385 (1991)
Leeb, S.B.: A conjoint pattern recognition approach to nonintrusive load monitoring. PhD thesis, Massachusetts Institute of Technology (1993)
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)
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)
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)
Bijker, A., Xia, X., Zhang, J.: Active power residential non-intrusive appliance load monitoring system. In: AFRICON 2009, pp. 1–6 (2009)
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)
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)
ISA Intelligent Sensing Anywhere, S.: Isa intelligent sensing anywhere (2009), http://www.isasensing.com/ [Online; accessed 18-October-2010].
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)
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
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)
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)
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)
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Figueiredo, M.B., de Almeida, A., Ribeiro, B. (2011). An Experimental Study on Electrical Signature Identification of Non-Intrusive Load Monitoring (NILM) Systems. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20267-4_4
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DOI: https://doi.org/10.1007/978-3-642-20267-4_4
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