A surrogate model for estimating extreme tower loads on wind turbines based on random forest proximities

DOI10.1080/02664763.2020.1815675OpenAlexW3083174200MaRDI QIDQ5861268

Victor Rohde, Mikkel Slot Nielsen

Publication date: 4 March 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1903.00251

zbMATH Keywords

matchingrandom forestssurrogate modelswind turbinesextreme load estimation

Mathematics Subject Classification ID

Applications of statistics in engineering and industry; control charts (62P30) Probabilistic models, generic numerical methods in probability and statistics (65C20) Matching models (91B68) Applications of statistics (62Pxx)

Uses Software

• IsolationForest

Cites Work

• Unnamed Item
• A survey of outlier detection methodologies
• A random forest guided tour
• The central role of the propensity score in observational studies for causal effects
• A survey on unsupervised outlier detection in high‐dimensional numerical data
• Random forests

This page was built for publication: A surrogate model for estimating extreme tower loads on wind turbines based on random forest proximities