US 11,886,843 B2
Systems and methods for utilizing machine learning to identify non-technical loss
Thomas M. Siebel, Woodside, CA (US); Edward Y. Abbo, Woodside, CA (US); Houman Behzadi, San Francisco, CA (US); Avid Boustani, Redwood City, CA (US); Nikhil Krishnan, Los Altos, CA (US); Kuenley Chiu, San Francisco, CA (US); Henrik Ohlsson, San Francisco, CA (US); Louis Poirier, San Francisco, CA (US); and Jeremy Kolter, Pittsburgh, PA (US)
Assigned to C3.ai, Inc., Redwood City, CA (US)
Filed by C3.AI, INC., Redwood City, CA (US)
Filed on Aug. 1, 2022, as Appl. No. 17/816,520.
Application 17/816,520 is a continuation of application No. 16/376,976, filed on Apr. 5, 2019, granted, now 11,449,315.
Application 16/376,976 is a continuation of application No. 14/495,848, filed on Sep. 24, 2014, granted, now 10,296,843, issued on May 21, 2019.
Prior Publication US 2023/0027296 A1, Jan. 26, 2023
Int. Cl. G06F 8/34 (2018.01); G06N 20/00 (2019.01); H04W 52/04 (2009.01); H04B 17/391 (2015.01); G06Q 50/06 (2012.01); G01R 21/00 (2006.01)
CPC G06F 8/34 (2013.01) [G01R 21/00 (2013.01); G06N 20/00 (2019.01); G06Q 50/06 (2013.01); H04B 17/391 (2015.01); H04W 52/04 (2013.01)] 24 Claims
OG exemplary drawing
 
1. A method for detecting non-technical energy loss, the method comprising:
generating, by a processor, a plurality of multidimensional representations of a plurality of energy use conditions based on a plurality of feature values extracted from energy data associated with a plurality of meters;
performing, by the processor, an unsupervised machine learning clustering process on the plurality of multidimensional representations to determine a plurality of clusters; and
outputting, by the processor to a user interface (UI), information that indicates one or more meters of the plurality of meters that are predicted to be associated with non-technical energy loss, the one or more meters associated with one or more energy use conditions identified based on the plurality of clusters.