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Title: Induced Markov chain for wind farm generation forecasting

Abstract

Systems and methods for forecasting power generation in a wind farm are disclosed. The systems and methods utilize an induced Markov chain model to generate a forecast of power generation of the wind farm. The forecast is at least one of a point forecast or a distributional forecast. Additionally, the systems and methods modify at least one of: (i) a generation of electricity at a power plant coupled to a common power grid as the wind farm; or (ii) a distribution of electricity in the common power grid based on the forecast of power generation of the wind farm. In an exemplary approach, utilizing the induced Markov chain model to generate the forecast may include determining a series of time adjacent power output measurements based on historical wind power measurements and calculating a time series of difference values based on the series of time adjacent power output measurements.

Inventors:
; ;
Issue Date:
Research Org.:
Arizona State Univ., Scottsdale, AZ (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1771482
Patent Number(s):
10796252
Application Number:
16/555,490
Assignee:
Arizona Board of Regents on Behalf of the University of Arizona (Scottsdale, AZ)
Patent Classifications (CPCs):
G - PHYSICS G01 - MEASURING G01W - METEOROLOGY
G - PHYSICS G06 - COMPUTING G06N - COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
DOE Contract Number:  
AR0000696; HDTRA1-13-1-0029
Resource Type:
Patent
Resource Relation:
Patent File Date: 08/29/2019
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY

Citation Formats

Werho, Trevor N., Zhang, Junshan, and Vittal, Vijay. Induced Markov chain for wind farm generation forecasting. United States: N. p., 2020. Web.
Werho, Trevor N., Zhang, Junshan, & Vittal, Vijay. Induced Markov chain for wind farm generation forecasting. United States.
Werho, Trevor N., Zhang, Junshan, and Vittal, Vijay. Tue . "Induced Markov chain for wind farm generation forecasting". United States. https://www.osti.gov/servlets/purl/1771482.
@article{osti_1771482,
title = {Induced Markov chain for wind farm generation forecasting},
author = {Werho, Trevor N. and Zhang, Junshan and Vittal, Vijay},
abstractNote = {Systems and methods for forecasting power generation in a wind farm are disclosed. The systems and methods utilize an induced Markov chain model to generate a forecast of power generation of the wind farm. The forecast is at least one of a point forecast or a distributional forecast. Additionally, the systems and methods modify at least one of: (i) a generation of electricity at a power plant coupled to a common power grid as the wind farm; or (ii) a distribution of electricity in the common power grid based on the forecast of power generation of the wind farm. In an exemplary approach, utilizing the induced Markov chain model to generate the forecast may include determining a series of time adjacent power output measurements based on historical wind power measurements and calculating a time series of difference values based on the series of time adjacent power output measurements.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2020},
month = {10}
}

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