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Related Experiment Video

Updated: Feb 20, 2026

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

Published on: February 13, 2018

A new method for wind speed forecasting based on copula theory.

Yuankun Wang¹, Huiqun Ma², Dong Wang¹

¹Key Laboratory of Surficial Geochemistry, Ministry of Education, Department of Hydrosciences, School of Earth Sciences and Engineering, State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing 210023, China.

Environmental Research

|October 27, 2017

Summary

This summary is machine-generated.

Determining representative wind speed is key for wind farm development. A new copula method improves accuracy by analyzing local and station wind data interactions, offering more reliable wind resource assessments.

Keywords:

Conditional distribution Copula method Wind farm Wind speed

Related Experiment Videos

Last Updated: Feb 20, 2026

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

Measurements of Waves in a Wind-wave Tank Under Steady and Time-varying Wind Forcing

Published on: February 13, 2018

Area of Science:

Renewable Energy
Meteorology
Statistical Modeling

Background:

Accurate wind resource assessment is vital for successful wind farm development.
Traditional linear regression methods often yield low correlations due to terrain and distance.
Existing methods struggle to capture complex interactions between local wind farm conditions and meteorological stations.

Purpose of the Study:

To introduce a novel copula-based method for determining representative annual wind speed.
To improve the accuracy of wind resource assessments in complex terrains.
To provide a more reasonable approach for obtaining representative wind data.

Main Methods:

Utilized the copula method to model the joint distribution of wind speed data.
Employed Kendall's tau to quantify the relationship between local anemometric tower and nearby meteorological station data.
Applied conditional distribution to derive representative wind speed data.

Main Results:

The copula method successfully determined the relationship between local and station wind data.
The method established the joint distribution without assuming independence between variables.
Conditional distribution provided more reasonable representative wind data compared to traditional methods.

Conclusions:

The proposed copula method offers a more accurate approach to wind resource assessment.
This study provides a valuable scientific reference for improving the reliability of wind farm development.
The method effectively addresses limitations of linear regression in complex wind farm environments.