Extracting features from an electrical signal of a non-intrusive load monitoring system



Improving energy efficiency by monitoring household electrical consumption is of significant importance with the present-day climate change concerns. A solution for the electrical consumption management problem is the use of a non-intrusive load monitoring system (NILM). This system captures the signals from the aggregate consumption, extracts the features from these signals and classifies the extracted features in order to identify the switched on appliances. An effective device identification (ID) requires a signature to be assigned for each appliance. Moreover, to specify an ID for each device, signal processing techniques are needed for extracting the relevant features. This paper describes a technique for the steady-states recognition in an electrical digital signal as the first stage for the implementation of an innovative NILM. Furthermore, the final goal is to develop an intelligent system for the identification of the appliances by automated learning. The proposed approach is based on the ratio value between rectangular areas defined by the signal samples. The computational experiments show the method effectiveness for the accurate steady-states identification in the electrical input signals.


Automated learning and identification, feature extraction and classification, non-intrusive load monitoring


Feature Extraction, Non-Intrusive Load Monitoring Systems


Proc Int Conf on Intelligent Data Engineering and Automated Learning, pp 210-217, LNCS 6283, September 2010

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Year 2016 : 1 citations

 Abubakar, I. , Khalid, S.N. , Mustafa, M.W. Recent approaches and applications of non-intrusive load monitoring
ARPN Journal of Engineering and Applied Sciences, Volume 11, Issue 7, April 2016, pp. 4609-4618

Year 2013 : 2 citations

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Year 2012 : 1 citations

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Year 2011 : 1 citations

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