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. 03, 2022

Filed:

Dec. 30, 2019
Applicant:

Sunpower Corporation, San Jose, CA (US);

Inventors:

Sugumar Murugesan, Foster City, CA (US);

Saravanan Thulasingam, Austin, TX (US);

Assignee:

SUNPOWER CORPORATION, San Jose, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06G 7/54 (2006.01); H02J 3/38 (2006.01); H02S 50/00 (2014.01); G06N 20/00 (2019.01); H02J 13/00 (2006.01); G06F 30/20 (2020.01);
U.S. Cl.
CPC ...
H02J 3/383 (2013.01); G06F 30/20 (2020.01); G06N 20/00 (2019.01); H02J 3/38 (2013.01); H02J 13/0079 (2013.01); H02S 50/00 (2013.01); H02J 2203/20 (2020.01); Y02E 10/56 (2013.01); Y02E 40/70 (2013.01); Y02E 60/00 (2013.01); Y04S 10/123 (2013.01); Y04S 40/20 (2013.01);
Abstract

Methods, systems, and computer storage media are disclosed for determining electric energy flow predictions for electric systems including photovoltaic solar systems. In some examples, a method is performed by a computer system and includes supplying a consumption time series and a predicted production time series for an electric system to a machine-learning predictor trained during a prior training phase using electric energy consumption training data and photovoltaic production training data. The consumption time series has a first data resolution, and the electric energy consumption training data and the photovoltaic production training data have a second data resolution greater than the first data resolution. The method includes determining, using an output of the machine-learning predictor, a predicted import time series of electric import values each specifying an amount of electric energy predicted to be imported by the electric system with a prospective photovoltaic solar system installed.


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