CISUC

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

Authors

Abstract

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.

Keywords

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

Subject

Feature Extraction, Non-Intrusive Load Monitoring Systems

Conference

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


Cited by

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

 Wong, Y. F.; Ahmet Sekercioglu, Y.; Drummond, T.; Wong, V. S., ``Recent approaches to non-intrusive load monitoring techniques in residential settings,'' IEEE Symposium on Computational Intelligence Applications In Smart Grid, 2013, pp.73-79, 2013, doi: 10.1109/CIASG.2013.6611501

 Wong, V. S.; Wong, Y. F.; Drummond, T.; Ahmet Sekercioglu, Y., ``A fast multiple appliance detection algorithm for non-intrusive load monitoring'', IEEE Symposium on Computational Intelligence Applications In Smart Grid, 2013, pp.80,86, 2013, doi: 10.1109/CIASG.2013.6611502

Year 2012 : 1 citations

 S. Rahimi. "Usage Monitoring of Electrical Devices in a Smart Home." Masters Dissertation, Carleton University, Canada (2012)

Year 2011 : 1 citations

 Chee X. M.; Le, C. V.; Zhang, D. H.; Luo, M.; Pang, C. K.; ``Intelligent identification of manufacturing operations using in-situ energy measurement in industrial injection moulding machines,'' Annual Conference on IEEE Industrial Electronics Society , pp.4284-4289, 2011