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:
Mar. 24, 2020

Filed:

Feb. 07, 2017
Applicant:

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

Inventors:

Varun Badrinath Krishna, Urbana, IL (US);

Younghun Kim, White Plains, NY (US);

Tarun Kumar, Mohegan Lake, NY (US);

Wander S. Wadman, New York, NY (US);

Kevin W. Warren, Hopewell Junction, NY (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
F03D 17/00 (2016.01); G06N 3/04 (2006.01); F03D 7/02 (2006.01); F03D 7/04 (2006.01); G06N 10/00 (2019.01); G06N 3/08 (2006.01);
U.S. Cl.
CPC ...
F03D 17/00 (2016.05); F03D 7/028 (2013.01); F03D 7/046 (2013.01); G06N 3/04 (2013.01); G06N 3/084 (2013.01); G06N 10/00 (2019.01); F05B 2260/821 (2013.01); F05B 2260/84 (2013.01); F05B 2270/32 (2013.01); F05B 2270/335 (2013.01); Y02A 30/12 (2018.01); Y02E 10/723 (2013.01);
Abstract

Historical electrical power output measurements of a wind turbine for a time period immediately preceding a specified past time are received. Historical wind speed micro-forecasts for the geographic location of the wind turbine, for a time period immediately preceding the specified past time and for a time period immediately following the specified past time are received. Based on the historical electrical power output measurements and the historical wind speed micro-forecasts, a trained machine learning model for predicting wind power output of the wind turbine is generated. Real-time electrical power output measurements of the wind turbine and real-time wind speed micro-forecasts for the geographic location of the wind turbine are received. Using the trained machine learning model with the real-time electrical power output measurements of the wind turbine and the real-time wind speed micro-forecasts, a wind power output forecast for the wind turbine at a future time is outputted.


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