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:
Dec. 31, 2019

Filed:

Jan. 23, 2017
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Aanchal Goyal, White Plains, NY (US);

Fook-Luen Heng, Yorktown Heights, NY (US);

Younghun Kim, White Plains, NY (US);

Tarun Kumar, Mohegan Lake, NY (US);

Mark A. Lavin, Katonah, NY (US);

Srivats Shukla, Yorktown Heights, NY (US);

Wander S. Wadman, Bussum, NL;

Kevin Warren, Hopewell Junction, NY (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06G 7/54 (2006.01); G06F 17/50 (2006.01);
U.S. Cl.
CPC ...
G06F 17/5009 (2013.01); G06F 2217/10 (2013.01); G06F 2217/78 (2013.01); Y02E 60/76 (2013.01); Y04S 40/22 (2013.01);
Abstract

Embodiments herein relate to improving a stochastic forecast for uncertain power generations and demands to quantify an effect on an electrical power grid. To improve the stochastic forecast, a method includes fitting marginal distributions to data of the uncertain power generation and demand by power generation and demand nodes of the electrical power grid. The power generation and demand nodes provide corresponding uncertain power generation and demand based on a renewable energy source. The method also includes determining a correlation structure between the power generation and demand nodes by transforming the data from marginal distributions to a second distribution and by fitting a multivariate time series on transformed data. The method also includes simulating multivariate stochastic forecast with an improved correlation structure.


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