An Experimental Study on Electrical Signature Identification of non-intrusive load monitoring (NILM) systems



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.


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


Non-Intrusive Load Monitoring Systems


Proc Intl Conf on Adaptive and Natural Computing Algorithms, pp 31-40, Part II, LNCS 6594, April 2011

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