A Versatile Clustering Method for Electricity Consumption Pattern Analysis in Households

Analysis and modeling of electric energy demand is indispensable for power planning, operation, facility investment, and urban planning. Because of recent development of renewable energy generation systems oriented for households, there is also a great demand for analysing the electricity usage and optimizing the way to install electricity generation systems for each household. In this study, employing statistical techniques, a method to model daily consumption patterns in households and a method to extract a small number of their typical patterns are presented. The electricity consumption patterns in a household is modeled by a mixture of Gaussian distributions. Then, using the symmetrized generalized Kullback-Leibler divergence as a distance measure of the distributions, typical patterns of the consumption are extracted by means of hierarchical clustering. The statistical modeling of daily consumption patterns allows us to capture essential similarities of the patterns. By experiments using a large-scale dataset including about 500 houses' consumption records in a suburban area in Japan, it is shown that the proposed method is able to extract typical consumption patterns.

[1]  J. H. Ward.  Hierarchical Grouping to Optimize an Objective Function , 1963 .

[2]  John R. Hershey,et al.  Approximating the Kullback Leibler Divergence Between Gaussian Mixture Models , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[3]  R. P. Saini,et al.  Sizing of integrated renewable energy system based on load profiles and reliability index for the state of Uttarakhand in India , 2011 .

[4]  José Roberto Moreira,et al.  Renewable energy markets in developing countries , 2003 .

[5]  Ali Sayigh.  WITHDRAWN: Worldwide progress in renewable energy , 2009 .

[6]  Eamonn J. Keogh,et al.  Iterative Deepening Dynamic Time Warping for Time Series , 2002, SDM.

[7]  R. Mojena,et al.  Hierarchical Grouping Methods and Stopping Rules: An Evaluation , 1977, Comput. J..

[8]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[9]  H. Akaike.  A new look at the statistical model identification , 1974 .

[10]  Ovidiu Ivanov.  LOAD PROFILING BY SELF ORGANIZATION WITH AFFINITY CONTROL STRATEGY , 2008 .

[11]  Shonali Pachauri,et al.  An Energy Analysis of Household Consumption , 2006 .

[12]  W. E. Alnaser,et al.  A “Comprehensive” Model for Accelerating the Building IntegratedPhotovoltaic (BIPV) / Wind Turbine (BIWT) Construction Projects in theKingdom of Bahrain , 2009 .

[13]  Regina Lamedica,et al.  A bottom-up approach to residential load modeling , 1994 .

[14]  Iris Grossmann,et al.  Indicators to determine winning renewable energy technologies with an application to photovoltaics. , 2010, Environmental science & technology.

[15]  Anil K. Jain,et al.  Algorithms for Clustering Data , 1988 .

[16]  Eamonn J. Keogh,et al.  Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases , 2001, Knowledge and Information Systems.

[17]  Zhe Li,et al.  Domestic application of micro wind turbines in Ireland: Investigation of their economic viability , 2012 .

[18]  Constantine T. Samarasa.  Learning from wind : A framework for effective low-carbon energy diffusion , 2006 .

[19]  Jorma Laaksonen,et al.  LVQ_PAK: The Learning Vector Quantization Program Package , 1996 .

[20]  Eamonn J. Keogh,et al.  Scaling up dynamic time warping for datamining applications , 2000, KDD '00.

[21]  Jukka Paatero,et al.  A model for generating household electricity load profiles , 2006 .

[22]  Robert Tibshirani,et al.  Estimating the number of clusters in a data set via the gap statistic , 2000 .

[23]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[24]  N. Athula Kulatunga,et al.  Smart meter based tools to enhance feedback oriented DSM with time of use rates , 2010, 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe).

[25]  M. L. Baughman,et al.  Optimizing combined cogeneration and thermal storage systems , 1989 .

[26]  M. Do.  Fast approximation of Kullback-Leibler distance for dependence trees and hidden Markov models , 2003, IEEE Signal Processing Letters.

[27]  Mihai Gavrilas,et al.  Identifying typical load profiles using neural-fuzzy models , 2001, 2001 IEEE/PES Transmission and Distribution Conference and Exposition. Developing New Perspectives (Cat. No.01CH37294).

[28]  Sergio Cruces,et al.  Generalized Alpha-Beta Divergences and Their Application to Robust Nonnegative Matrix Factorization , 2011, Entropy.

[29]  Patrick J. F. Groenen,et al.  Modern Multidimensional Scaling: Theory and Applications , 2003 .

[30]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[31]  Joseph B. Kruskall,et al.  The Symmetric Time-Warping Problem : From Continuous to Discrete , 1983 .

[32]  Jahangir Hossain,et al.  Determination of Typical Load Profile of Consumers Using Fuzzy C-Means Clustering Algorithm , 2011 .

[33]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[34]  G. Giacomelli,et al.  The energy problem , 2009, 0901.3711.

[35]  Julia V. Gallenkamp,et al.  Exploring the Role of Control - Smart Meter Acceptance of Residential Consumers , 2010, AMCIS.

[36]  J. Weerahewa,et al.  Household Energy Consumption Patterns in Sri Lanka , 2010 .

[37]  Martin L. Baughman,et al.  Optimizing Combined Cogeneration and Thermal Storage Systems: An Engineering Economics Approach , 1989, IEEE Power Engineering Review.

[38]  Z. Zakaria,et al.  Two-stage fuzzy clustering approach for load profiling , 2009, 2009 44th International Universities Power Engineering Conference (UPEC).

[39]  Eatzaz Ahmad,et al.  Understanding household consumption patterns in Pakistan , 2007 .

[40]  Mark D. Apperley,et al.  Power and energy visualization for the micro-management of household electricity consumption , 2010, AVI.

[41]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[42]  F. Gubina,et al.  Consumers' load profile determination based on different classification methods , 2003, 2003 IEEE Power Engineering Society General Meeting (IEEE Cat. No.03CH37491).

[43]  Don H. Johnson,et al.  Symmetrizing the Kullback-Leibler Distance , 2001 .

[44]  Christos Faloutsos,et al.  Efficient Similarity Search In Sequence Databases , 1993, FODO.

[45]  Snuller Price,et al.  Smart meter, customer choice and profitable time-of-use rate option , 1999 .

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

[47]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.