CUSTOMER SEGMENTATION BASED ON SMART METER DATA

1 . A method comprising:

obtaining energy consumption data, the energy consumption data comprising a plurality of value days, each value day from the plurality of value days being associated with a customer profile from a plurality of customer profiles, each value day from the plurality of value days comprising a plurality of energy consumption measurements at different times during a 24-hour period;

grouping the plurality of value days into a set of clusters, each cluster in the set of clusters comprising a subset of the plurality of the value days; and

identifying a customer profile as associated with a cluster from the set of clusters based on results of examining value days associated with the customer profile.

2 . The method of claim 1 , comprising receiving information defining a time frame, wherein the obtaining of energy consumption data comprises obtaining the energy consumption data for the time frame.

3 . The method of claim 1 , wherein the identifying of the customer profile as associated with the cluster comprises determining that a predetermined portion of the value days associated with the customer profile are from the cluster.

4 . The method of claim 1 , wherein value days in a cluster from the set of clusters are characterized by similar respective energy consumption measurements.

5 . The method of claim 4 , wherein similarity between energy consumption measurements is determined based on comparing absolute values of respective energy consumption measurements.

6 . The method of claim 4 , wherein similarity between energy consumption measurements is determined based on comparing normalized values of respective energy consumption measurements.

7 . The method of claim 1 , wherein the grouping of the plurality of value days into a set of clusters is performed periodically.

8 . The method of claim 1 comprising generating a histogram illustrating respective numbers of customers associated with ranges of energy consumption during a time period within a 24 hour period.

9 . The method of claim 1 , comprising:

generating a visual representation of energy consumption measurements associated with a cluster from the set of clusters; and

accessing customer profiles identified as associated with the cluster;

generating a multi-dimensional chart illustrating statistics associated with the assessed customer profiles.

10 . A method comprising:

accessing revenue data for a period of time;

accessing energy purchase cost information for the period of time;

accessing smart meter data for a customer for the period of time; and

determining relationship of the energy purchase cost information and the smart meter data for the customer.

11 . A computer-implemented system comprising:

a communications module to obtain energy consumption data, the energy consumption data comprising a plurality of value days, each value day from the plurality of value days being associated with a customer profile from a plurality of customer profiles, each value day from the plurality of value days comprising a plurality of energy consumption measurements at different times during a 24-hour period;

a clustering module to group the plurality of value days into a set of clusters, each cluster in the set of clusters comprising a subset of the plurality of the value days; and

a matching module to identify a customer profile as associated with a cluster from the set of clusters based on results of examining value days associated with the customer profile.

12 . The system of claim 11 , wherein the communications module is to receive information defining a time frame, wherein the energy consumption data is the energy consumption data for the time frame.

13 . The system of claim 11 , wherein the matching module is to determine that a predetermined portion of the value days associated with the customer profile are from the cluster.

14 . The system of claim 11 , wherein value days in a cluster from the set of clusters are characterized by similar respective energy consumption measurements

15 . The system of claim 14 , wherein the matching module is to determine similarity between energy consumption measurements based on comparing absolute values of respective energy consumption measurements.

16 . The system of claim 14 , wherein the matching module is to determine similarity between energy consumption measurements based on comparing normalized values of respective energy consumption measurements.

17 . The system of claim 11 , wherein the clustering module is to perform grouping of the plurality of value days into a set of clusters periodically.

18 . The system of claim 11 , comprising a histogram module to generate a histogram illustrating respective numbers of customers associated with ranges of energy consumption during a time period within a 24 hour period.

19 . The system of claim 11 , comprising a multi-dimensional chart module to:

access customer profiles identified as associated with a cluster from the set of clusters; and

generate a multi-dimensional chart illustrating statistics associated with the assessed customer profiles.

20 . A machine-readable non-transitory medium having instruction data to cause a machine to:

obtain energy consumption data, the energy consumption data comprising a plurality of value days, each value day from the plurality of value days being associated with a customer profile from a plurality of customer profiles, each value day from the plurality of value days comprising a plurality of energy consumption measurements at different times during a 24-hour period;

group the plurality of value days into a set of clusters, each cluster in the set of clusters comprising a subset of the plurality of the value days; and

identify a customer profile as associated with a cluster from the set of clusters based on results of examining value days associated with the customer profile.