风速
风力发电
决策树
气象学
风力资源评估
环境科学
功率(物理)
转子(电动)
计算机科学
风向
机器学习
工程类
地理
机械工程
电气工程
物理
量子力学
作者
Christiana Sasser,Meilin Yu,R. M. González Delgado
标识
DOI:10.1016/j.renene.2021.10.034
摘要
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.
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