TY - JOUR
T1 - A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset
AU - Kartal, Serkan
AU - Basu, Sukanta
AU - Watson, Simon J.
PY - 2023
Y1 - 2023
N2 - Peak wind gust (Wp) is a crucial meteorological variable for wind farm planning and operations. However, for many wind farm sites, there is a dearth of on-site measurements of Wp. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset and, in turn, generates multi-year, site-specific Wp series. Through a systematic feature importance study, we also identify the most relevant meteorological variables for Wp estimation. The INTRIGUE approach outperforms the baseline predictions for all wind gust conditions. However, the performance of this proposed approach and the baselines for extreme conditions (i.e., Wp>20 m s−1) is less satisfactory.
AB - Peak wind gust (Wp) is a crucial meteorological variable for wind farm planning and operations. However, for many wind farm sites, there is a dearth of on-site measurements of Wp. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset and, in turn, generates multi-year, site-specific Wp series. Through a systematic feature importance study, we also identify the most relevant meteorological variables for Wp estimation. The INTRIGUE approach outperforms the baseline predictions for all wind gust conditions. However, the performance of this proposed approach and the baselines for extreme conditions (i.e., Wp>20 m s−1) is less satisfactory.
UR - http://www.scopus.com/inward/record.url?scp=85178224315&partnerID=8YFLogxK
U2 - 10.5194/wes-8-1533-2023
DO - 10.5194/wes-8-1533-2023
M3 - Article
SN - 2366-7443
VL - 8
SP - 1533
EP - 1551
JO - Wind Energy Science
JF - Wind Energy Science
IS - 10
M1 - 8
ER -