Clustering Time-Series Energy Data from Smart Meters
This work addresses energy efficiency programs for commercial and industrial customers, but it is incremental as it applies existing clustering methods to new smart meter data.
The paper tackled the problem of identifying patterns and trends in energy usage profiles from smart meters by clustering time-series data, resulting in accurate grouping of accounts with similar energy usage patterns, as demonstrated on data from U.S. power utilities.
Investigations have been performed into using clustering methods in data mining time-series data from smart meters. The problem is to identify patterns and trends in energy usage profiles of commercial and industrial customers over 24-hour periods, and group similar profiles. We tested our method on energy usage data provided by several U.S. power utilities. The results show accurate grouping of accounts similar in their energy usage patterns, and potential for the method to be utilized in energy efficiency programs.