Household Energy Consumption Segmentation Using Hourly Data

The increasing US deployment of residential advanced metering infrastructure (AMI) has made hourly energy consumption data widely available. Using CA smart meter data, we investigate a household electricity segmentation methodology that uses an encoding system with a pre-processed load shape dictionary. Structured approaches using features derived from the encoded data drive five sample program and policy relevant energy lifestyle segmentation strategies. We also ensure that the methodologies developed scale to large data sets.

[1]  G. Chicco,et al.  Comparisons among clustering techniques for electricity customer classification , 2006, IEEE Transactions on Power Systems.

[2]  Steven J. Moss,et al.  Market Segmentation and Energy Efficiency Program Design , 2008 .

[3]  Z. Vale,et al.  An electric energy consumer characterization framework based on data mining techniques , 2005, IEEE Transactions on Power Systems.

[4]  W. C. Beattie,et al.  Statistical electricity demand modelling from consumer billing data , 1986 .

[5]  Bernadette Sütterlin,et al.  Who puts the most energy into energy conservation? A segmentation of energy consumers based on energy-related behavioral characteristics , 2011 .

[6]  Christoph Flath,et al.  Cluster Analysis of Smart Metering Data , 2012, Business & Information Systems Engineering.

[7]  T. Sanquist,et al.  Lifestyle factors in U.S. residential electricity consumption , 2012 .

[8]  Mikko Kolehmainen,et al.  Feature-Based Clustering for Electricity Use Time Series Data , 2009, ICANNGA.

[9]  C. F. Walker,et al.  Residential Load Shape Modelling Based on Customer Behavior , 1985, IEEE Transactions on Power Apparatus and Systems.

[10]  G. Coke,et al.  Random effects mixture models for clustering electrical load series , 2010 .

[11]  B.D. Pitt,et al.  Application of data mining techniques to load profiling , 1999, Proceedings of the 21st International Conference on Power Industry Computer Applications. Connecting Utilities. PICA 99. To the Millennium and Beyond (Cat. No.99CH36351).

[12]  Sanjiv K. Bhatia Adaptive K-Means Clustering , 2004, FLAIRS Conference.

[13]  Loren Lutzenhiser,et al.  Behavioral Assumptions Underlying California Residential Sector Energy Efficiency Programs , 2009 .

[14]  P. Postolache,et al.  Load pattern-based classification of electricity customers , 2004, IEEE Transactions on Power Systems.

[15]  C. Senabre,et al.  Classification, Filtering, and Identification of Electrical Customer Load Patterns Through the Use of Self-Organizing Maps , 2006, IEEE Transactions on Power Systems.

[16]  N.D. Hatziargyriou,et al.  Two-Stage Pattern Recognition of Load Curves for Classification of Electricity Customers , 2007, IEEE Transactions on Power Systems.

[17]  B. De Moor,et al.  Short-term load forecasting, profile identification, and customer segmentation: a methodology based on periodic time series , 2005, IEEE Transactions on Power Systems.