Random forest based adaptive non-intrusive load identification

Non-intrusive load monitoring (NEM) is a load monitoring technique proposed to be used in today's residential energy auditor. It is expected to automatically provide the information of the type, energy consumption, and operation status of the electric loads without getting access to the loads. However, there still not exists any commercialized product so far, mainly because of the extraordinary large load sets comparing with the limited learning data. The fast emerging of new types of loads further aggravates the problem. This paper proposes an adaptive non-intrusive load identification model to address this problem. The proposed model is not dedicated to identify all the loads around the world, but it will grasp knowledge from samples that are not identified in the real application, and gradually form a new learning procedure so as to identify more and more new samples correctly. Random forest algorithm is introduced here to realize the objective and a case study is carried out to verify the effectiveness of the model.

[1]  Jian Liang,et al.  Load Signature Study—Part I: Basic Concept, Structure, and Methodology , 2010, IEEE Transactions on Power Delivery.

[2]  K. Musierowicz,et al.  A fuzzy logic- based algorithm for discrimination of damaged line during intermittent earth faults , 2005, 2005 IEEE Russia Power Tech.

[3]  Balasubramanian Karuppanan,et al.  Improvements in home automation strategies for designing apparatus for efficient smart home , 2008, IEEE Transactions on Consumer Electronics.

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

[5]  Horst Bischof,et al.  On-line Random Forests , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[6]  Seunghyun Park,et al.  Concurrent simulation platform for energy-aware smart metering systems , 2010, IEEE Transactions on Consumer Electronics.

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

[8]  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).

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

[10]  H. Y. Lam,et al.  A Novel Method to Construct Taxonomy Electrical Appliances Based on Load Signaturesof , 2007, IEEE Transactions on Consumer Electronics.

[11]  M. Baranski,et al.  Nonintrusive appliance load monitoring based on an optical sensor , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[12]  Andrew W. Moore,et al.  X-means: Extending K-means with Efficient Estimation of the Number of Clusters , 2000, ICML.

[13]  Choong Seon Hong,et al.  Design and Implementation of Control Mechanism for Standby Power Reduction , 2008, IEEE Transactions on Consumer Electronics.

[14]  E.A. Cano Plata,et al.  Power quality assessment and load identification , 2000, Ninth International Conference on Harmonics and Quality of Power. Proceedings (Cat. No.00EX441).

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

[16]  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).

[17]  Michael Baranski,et al.  Detecting patterns of appliances from total load data using a dynamic programming approach , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[18]  Loi Lei Lai,et al.  Harmonics load signature recognition by wavelets transforms , 2000, DRPT2000. International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. Proceedings (Cat. No.00EX382).

[19]  Yi Du,et al.  A review of identification and monitoring methods for electric loads in commercial and residential buildings , 2010, 2010 IEEE Energy Conversion Congress and Exposition.

[20]  Loi Lei Lai,et al.  Wavelet feature vectors for neural network based harmonics load recognition , 2000 .

[21]  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).

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

[23]  Yi Yang,et al.  Front-End Electronic Circuit Topology Analysis for Model-Driven Classification and Monitoring of Appliance Loads in Smart Buildings , 2012, IEEE Transactions on Smart Grid.

[24]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .

[25]  Mark Lucente,et al.  Exploration on Load Signatures , 2004 .

[26]  B.A. Smith,et al.  Using a rule-based algorithm to disaggregate end-use load profiles from premise-level data , 1991, IEEE Computer Applications in Power.