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A Circular-Linear Probabilistic Model Based on Nonparametric Copula with Applications to Directional Wind Energy
1College of Science, Inner Mongolia University of Technology, Hohhot 010051, China.
Entropy (Basel, Switzerland)
|June 26, 2024
Summary
A new nonparametric copula model accurately estimates wind speed and direction probability, revealing abundant wind resources in Inner Mongolia. This improves wind farm siting and turbine design for optimal energy capture.
Area of Science:
- Renewable Energy Systems
- Statistical Modeling
- Atmospheric Science
Background:
- Directional wind energy assessment relies on joint probability density functions of wind speed and direction.
- Accurate modeling is crucial for optimizing wind farm performance and turbine design.
Purpose of the Study:
- To propose and investigate a nonparametric joint probability estimation system for wind velocity and direction using copulas.
- To compare the proposed method with parametric copula models and models ignoring dependency.
Main Methods:
- Developed a nonparametric copula method using optimal bandwidth algorithms and transformation techniques.
- Implemented the Kernel Density Estimation-Copula-Cross-Validation (KDE-COP-CV) model.
- Analyzed joint probability distributions and correlation between wind speed and direction.
Main Results:
- The nonparametric copula model significantly outperforms other methods in fitting joint probability distributions.
- The KDE-COP-CV model enables reliable analysis of wind power density fluctuations with wind direction.
- Inner Mongolia exhibits abundant wind resources, with peak power density linked to direction at maximum speeds.
Conclusions:
- The nonparametric copula approach is advantageous for directional wind energy assessment.
- Wind resources in the studied Inner Mongolia regions are concentrated in NW and W directions.
- Findings support enhanced accuracy in wind farm micro-siting and turbine generator optimization.
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