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:

Apr. 27, 2018
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 7/00 (2006.01); F03D 7/02 (2006.01); G05B 13/02 (2006.01); G05B 19/416 (2006.01); F03D 17/00 (2016.01); F03D 9/25 (2016.01); F03D 7/04 (2006.01);
U.S. Cl.
CPC ...
F03D 7/0284 (2013.01); F03D 7/046 (2013.01); F03D 7/047 (2013.01); F03D 7/048 (2013.01); F03D 9/25 (2016.05); F03D 17/00 (2016.05); G05B 13/026 (2013.01); G05B 19/416 (2013.01); F05B 2260/821 (2013.01); F05B 2270/1033 (2013.01); F05B 2270/32 (2013.01); F05B 2270/337 (2013.01); F05B 2270/404 (2013.01); F05B 2270/709 (2013.01); G05B 2219/2619 (2013.01); Y02A 30/12 (2018.01); Y02E 10/723 (2013.01); Y02E 10/725 (2013.01);
Abstract

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


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