Development of a real-time non-intrusive appliance load monitoring system: An application level model

Abstract The research “Behavior Change and Energy Use” (US Department of Energy and Climate Change, 2011) [1] shows that with better information in the monthly electricity bill, the Energy Performance Certificate (EPC) can encourage people to reduce their energy usage. That is why smart meters - the emerging technology to help people to know their monthly energy consumption, are gradually replacing mechanical power meters. In this paper, we investigate a special energy monitoring process named Non-Intrusive Appliance Load Monitoring (NIALM), which is potentially the best method to give consumers pertinent information with respect to power consumption. However, real-time feedback feature in a low cost NIALM system is still a big challenge in such technology because of the complication in NIALM’s algorithms. System on Chip (SoC) technology can solve this challenge. Besides including high-speed interconnection and multi-processors, integrating Field-Programmable Gate Array (FPGA) into SoCs may be the most important evolution, which provides developers a powerful tool to develop a low cost but high performance system. Therefore, in this paper we proposed a development of a real-time NIALM system based on the SoC with FPGA acceleration.

[1]  Karl Aberer,et al.  Sustainable energy consumption monitoring in residential settings , 2013, 2013 Proceedings IEEE INFOCOM.

[2]  Eric C. Larson,et al.  Disaggregated End-Use Energy Sensing for the Smart Grid , 2011, IEEE Pervasive Computing.

[3]  Eric Dekneuvel,et al.  Using FPGA for real time power monitoring in a NIALM system , 2013, 2013 IEEE International Symposium on Industrial Electronics.

[4]  Edward A. Lee,et al.  Introduction to Embedded Systems - A Cyber-Physical Systems Approach , 2013 .

[5]  Steven B. Leeb,et al.  Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms , 1996 .

[6]  Eric Dekneuvel,et al.  Intelligent Sensors: Analysis and Design , 2005, The Industrial Information Technology Handbook.

[7]  M. Baranski,et al.  Genetic algorithm for pattern detection in NIALM systems , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[8]  Gilles Jacquemod,et al.  An innovative non-intrusive load monitoring system for commercial and industrial application , 2012, The 2012 International Conference on Advanced Technologies for Communications.

[9]  Bernardete Ribeiro,et al.  Home electrical signal disaggregation for non-intrusive load monitoring (NILM) systems , 2012, Neurocomputing.

[10]  Sanghamitra Bandyopadhyay,et al.  Classification and learning using genetic algorithms - applications in bioinformatics and web intelligence , 2007, Natural computing series.

[11]  Tommi S. Jaakkola,et al.  Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation , 2012, AISTATS.

[12]  Alex Rogers,et al.  Non-Intrusive Load Monitoring Using Prior Models of General Appliance Types , 2012, AAAI.

[13]  Gregory D. Abowd,et al.  At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line (Nominated for the Best Paper Award) , 2007, UbiComp.

[14]  Shinkichi Inagaki,et al.  Validation of Nonintrusive Appliance Load Monitoring Based on Integer Programming , 2008 .

[15]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[16]  Lucio Soibelman,et al.  Enhancing Electricity Audits in Residential Buildings with Nonintrusive Load Monitoring , 2010 .

[17]  S.B. Leeb,et al.  Transient event detection for nonintrusive load monitoring and demand side management using voltage distortion , 2006, Twenty-First Annual IEEE Applied Power Electronics Conference and Exposition, 2006. APEC '06..

[18]  Michael Stonebraker,et al.  The 8 requirements of real-time stream processing , 2005, SGMD.