Power signature-based non-intrusive load disaggregation

For more efficient energy consumption, it is crucial to have accurate information on how power is being consumed through a single power measurement. It benefits both market participants such as retailers, network businesses and also power consumers. This means that the metering device must not only be able to distinguish between different loads on a common circuit, but also decipher their respective power consumption. This paper gives our investigation on power load signature and power decomposition. In particular, the paper focuses on the development of power decomposition algorithm based on support vector machine (SVM), i.e., estimating the power proportion of constant power (CP) loads to constant impedance (CI) loads. The power decomposition algorithm in this paper is working on steady-state power load signal.

[1]  Bijaya K. Panigrahi,et al.  Optimal short-term hydrothermal generation scheduling using modified seeker optimisation algorithm , 2012, Int. J. Model. Identif. Control..

[2]  F. Sultanem,et al.  Using appliance signatures for monitoring residential loads at meter panel level , 1991 .

[3]  S. Drenker,et al.  Nonintrusive monitoring of electric loads , 1999 .

[4]  Glenn Platt,et al.  HVAC control strategies for thermal comfort and indoor air quality , 2011 .

[5]  Jiaming Li,et al.  Agent-Based Distributed Energy Management , 2007, Australian Conference on Artificial Intelligence.

[6]  Ying Guo,et al.  Directed Self-assembly of 2-Dimensional Mesoblocks Using Top-Down/Bottom-Up Design , 2004, Engineering Self-Organising Systems.

[7]  Yu Tang,et al.  Modelling of a traffic cell based on a recurrent-neural network , 2011, Int. J. Model. Identif. Control..

[8]  Xiaoqi Chen,et al.  Identification of an unmanned helicopter system using optimised neural network structure , 2012, Int. J. Model. Identif. Control..

[9]  A. Albicki,et al.  Data extraction for effective non-intrusive identification of residential power loads , 1998, IMTC/98 Conference Proceedings. IEEE Instrumentation and Measurement Technology Conference. Where Instrumentation is Going (Cat. No.98CH36222).

[10]  Josh Wall,et al.  Adaptive HVAC zone modeling for sustainable buildings , 2010 .

[11]  Jiaming Li,et al.  Set-Points Based Optimal Multi-Agent Coordination for Controlling Distributed Energy Loads , 2009, 2009 Third IEEE International Conference on Self-Adaptive and Self-Organizing Systems.

[12]  Glenn Platt,et al.  Dynamic zone modelling for HVAC system control , 2010, Int. J. Model. Identif. Control..

[13]  G. Platt,et al.  The Virtual Power Station , 2008, 2008 IEEE International Conference on Sustainable Energy Technologies.

[14]  Hossein Ebrahimpour-Komleh,et al.  A Positioning Method in Wireless Sensor Networks Using Genetic Algorithms , 2010, J. Digit. Content Technol. its Appl..

[15]  Suhuai Luo,et al.  An Approach of Household Power Appliance Monitoring Based on Machine Learning , 2012, 2012 Fifth International Conference on Intelligent Computation Technology and Automation.

[16]  Gerhard P. Hancke,et al.  Using neural networks for non-intrusive monitoring of industrial electrical loads , 1994, Conference Proceedings. 10th Anniversary. IMTC/94. Advanced Technologies in I & M. 1994 IEEE Instrumentation and Measurement Technolgy Conference (Cat. No.94CH3424-9).

[17]  Ning Xiong,et al.  Multi-sensor management for information fusion: issues and approaches , 2002, Inf. Fusion.

[18]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[19]  Ying Guo,et al.  Evolutionary Optimisation of Distributed Energy Resources , 2005, Australian Conference on Artificial Intelligence.

[20]  Lei Jiang,et al.  Power Load Event Detection and Classification Based on Edge Symbol Analysis and Support Vector Machine , 2012, Appl. Comput. Intell. Soft Comput..

[21]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[22]  A. Albicki,et al.  Nonintrusive identification of electrical loads in a three-phase environment based on harmonic content , 2000, Proceedings of the 17th IEEE Instrumentation and Measurement Technology Conference [Cat. No. 00CH37066].

[23]  A. Albicki,et al.  Algorithm for nonintrusive identification of residential appliances , 1998, ISCAS '98. Proceedings of the 1998 IEEE International Symposium on Circuits and Systems (Cat. No.98CH36187).

[24]  Steven B. Leeb,et al.  A conjoint pattern recognition approach to nonintrusive load monitoring , 1993 .

[25]  Jesse S. Jin,et al.  Sensor Data Fusion for Accurate Cloud Presence Prediction Using Dempster-Shafer Evidence Theory , 2010, Sensors.

[26]  Suhuai Luo,et al.  Literature review of power disaggregation , 2011, Proceedings of 2011 International Conference on Modelling, Identification and Control.

[27]  Yang Wang,et al.  Dynamic neural-fuzzified adaptive control of ship course with parametric modelling uncertainties , 2011, Int. J. Model. Identif. Control..

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

[29]  Radu Zmeureanu,et al.  Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses , 1999 .

[30]  Radu Zmeureanu,et al.  Nonintrusive load disaggregation computer program to estimate the energy consumption of major end uses in residential buildings , 2000 .

[31]  Jiaming Li,et al.  COORDINATION OF DISTRIBUTED ENERGY RESOURCE AGENTS , 2010, Appl. Artif. Intell..

[32]  Steven B. Leeb,et al.  Power signature analysis , 2003 .

[33]  Hong-Tzer Yang,et al.  Load Identification in Neural Networks for a Non-intrusive Monitoring of Industrial Electrical Loads , 2007, CSCWD.