Analysis and Clustering of Residential Customers Energy Behavioral Demand Using Smart Meter Data

Clustering methods are increasingly being applied to residential smart meter data, which provides a number of important opportunities for distribution network operators (DNOs) to manage and plan low-voltage networks. Clustering has a number of potential advantages for DNOs, including the identification of suitable candidates for demand response and the improvement of energy profile modeling. However, due to the high stochasticity and irregularity of household-level demand, detailed analytics are required to define appropriate attributes to cluster. In this paper, we present in-depth analysis of customer smart meter data to better understand the peak demand and major sources of variability in their behavior. We find four key time periods, in which the data should be analyzed, and use this to form relevant attributes for our clustering. We present a finite mixture model-based clustering, where we discover ten distinct behavior groups describing customers based on their demand and their variability. Finally, using an existing bootstrap technique, we show that the clustering is reliable. To the authors' knowledge, this is the first time in the power systems literature that the sample robustness of the clustering has been tested.

[1]  Ram Rajagopal,et al.  Household Energy Consumption Segmentation Using Hourly Data , 2014, IEEE Transactions on Smart Grid.

[2]  Matthew Rowe,et al.  Mathematical solutions for electricity networks in a low carbon future , 2013 .

[3]  Uwe Aickelin,et al.  An Approach for Assessing Clustering of Households by Electricity Usage , 2014, ArXiv.

[4]  Carolyn Geda.  Social Science Data Archives , 2010 .

[5]  Graeme Burt,et al.  Enhanced Load Profiling for Residential Network Customers , 2014, IEEE Transactions on Power Delivery.

[6]  Uwe Aickelin,et al.  Finding the Creatures of Habit; Clustering Households Based on Their Flexibility in Using Electricity , 2013, ArXiv.

[7]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[8]  Silvia Santini,et al.  Revealing Household Characteristics from Smart Meter Data , 2014 .

[9]  Ram Rajagopal,et al.  Smart Meter Driven Segmentation: What Your Consumption Says About You , 2013, IEEE Transactions on Power Systems.

[10]  José Luis Díez,et al.  Dynamic clustering segmentation applied to load profiles of energy consumption from Spanish customers , 2014 .

[11]  José G. Dias,et al.  Bootstrap methods for measuring classification uncertainty in latent class analysis , 2006 .

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

[13]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[14]  Mike Hazas,et al.  The significance of difference:understanding variation in household energy consumption , 2011 .

[15]  Mohamed Chaouch,et al.  Clustering-Based Improvement of Nonparametric Functional Time Series Forecasting: Application to Intra-Day Household-Level Load Curves , 2014, IEEE Transactions on Smart Grid.

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

[17]  Peter Grindrod,et al.  A new error measure for forecasts of household-level, high resolution electrical energy consumption , 2014 .

[18]  Thorsten Staake,et al.  Are domestic load profiles stable over time? An attempt to identify target households for demand side management campaigns , 2013, IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society.

[19]  P. Deb.  Finite Mixture Models , 2008 .

[20]  Michael Conlon,et al.  Characterising domestic electricity consumption patterns by dwelling and occupant socio-economic variables: An Irish case study , 2012 .

[21]  Bruce Stephen,et al.  Domestic Load Characterization Through Smart Meter Advance Stratification , 2012, IEEE Transactions on Smart Grid.

[22]  Gianfranco Chicco,et al.  Overview and performance assessment of the clustering methods for electrical load pattern grouping , 2012 .

[23]  Heng Huang,et al.  Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities , 2015, IEEE Transactions on Smart Grid.

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

[25]  Francisco C. Pereira,et al.  Using pattern recognition to identify habitual behavior in residential electricity consumption , 2012 .