Details:

Journal Title:
Renewable Energy
Personal Author:
Sasser, Christiana ; Yu, Meilin ; Delgado, Ruben
Sasser, Christiana ; Yu, Meilin ; Delgado, Ruben Less -
NOAA Program & Office:
Education and outreach
Description:
To mitigate uncertainties in wind resource assessments and to improve the estimation of energy production of a wind project, this work uses a decision tree machine learning model to assess the effectiveness of hub-height wind speed, rotor-equivalent wind speed, and lapse rate as variables in power prediction. Atmospheric data is used to train regression trees and correlate the power outputs to wind profiles and meteorological characteristics to be able to predict power responses according to physical patterns. The decision tree model was trained for four vertical wind profile classifications to showcase the need for multiple calculations of wind speed at various levels of the rotor layer. Results indicate that when compared to traditional power curve methods, the decision tree combining rotor-equivalent wind speed and lapse rate improves prediction accuracy by 22% for the given data-set, while also proving to be the most effective method in power prediction for all classified vertical wind profile types. Models incorporating lapse rate into predictions performed better than those without it, showing the importance of considering atmospheric criteria in wind power prediction analyses.
Keywords:
[+]
Renewable Energy, Sustainability and the Environment
Source:
Renewable Energy, 183, 491-501
DOI:
https://doi.org/10.1016/j.renene.2021.10.034
ISSN:
0960-1481
Format:
PDF
Publisher:
Elsevier BV
Document Type:
Journal Article
Funding:
Grant no. NA16SEC4810008
Rights Information:
Accepted Manuscript
Compliance:
Library
Main Document Checksum:
[+]
urn:sha256:2d493965bd93938974bb34c19b492293b30393cf083373ccd1a389c121fc134a
Download URL:
https://repository.library.noaa.gov/view/noaa/57628/noaa_57628_DS1.pdf
File Type:

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