The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
May. 14, 2019

Filed:

Jan. 06, 2015
Applicant:

Accenture Global Services Limited, Dublin, IE;

Inventors:

Ming Li, Beijing, CN;

Qin Zhou, Beijing, CN;

Zhihui Yang, Beijing, CN;

Ming Ye, Ghungzhou, CN;

Long He, Shanghai, CN;

Guang Lin, Shanghai, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 17/00 (2019.01); G06N 5/04 (2006.01); G06N 20/00 (2019.01); G06F 16/25 (2019.01); H02J 3/00 (2006.01); H02J 13/00 (2006.01); H02H 3/00 (2006.01); H02H 7/04 (2006.01);
U.S. Cl.
CPC ...
G06N 5/04 (2013.01); G06F 16/258 (2019.01); G06N 20/00 (2019.01); H02H 3/006 (2013.01); H02H 7/04 (2013.01); H02J 3/00 (2013.01); H02J 13/001 (2013.01); H02J 2003/003 (2013.01); H02J 2003/007 (2013.01); Y02E 60/725 (2013.01); Y02E 60/74 (2013.01); Y02E 60/76 (2013.01); Y04S 10/20 (2013.01); Y04S 10/30 (2013.01); Y04S 10/40 (2013.01); Y04S 40/22 (2013.01);
Abstract

A system can generate a heavy load pre-warning or an overload pre-warning for distribution transformers. Operation of the system can include selecting data records received from a plurality of data sources; converting the data records in the plurality of different data formats; filtering the data records in the database by using a predetermined threshold and matching each of the filtered data records with one of a plurality of distribution transformers; transforming the matched data records to a plurality of predefined predictor variables; selecting a subset of the plurality of predefined predictor variables; training, testing and tuning a model and forecasting at least one of heavy load or overload for each of the plurality of distribution transformers in a predetermined region.


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