An innovative forecasting model to predict wind energy | JoVE Visualize

Area of Science:

Renewable Energy Systems
Computational Intelligence
Time Series Forecasting

Background:

Global wind power construction is rapidly expanding, highlighting the need for reliable energy integration.
The inherent volatility and irregularity of wind power pose significant challenges for grid integration.
Accurate ultra-short-term wind speed prediction is crucial for managing wind power's impact on the grid.

Purpose of the Study:

To develop a novel, accurate, and competitive ultra-short-term wind speed prediction model.
To address limitations of existing models, including incomplete information extraction and complex optimization algorithms.
To improve the adaptability and predictive capability of wind speed forecasting.

Main Methods:

Utilized Variational Mode Decomposition (VMD) to separate wind speed signals into high-frequency and low-frequency components.
Employed Principal Component Analysis (PCA) and spectral clustering for extracting and classifying wind speed influencing factors.
Developed an adaptive improved Particle Swarm Optimization (PSO) algorithm to enhance convergence speed and optimization effectiveness.
Implemented a rolling training method to dynamically adjust training sample sizes.

Main Results:

The proposed combined model effectively extracts comprehensive information from wind speed data and its influencing factors.
The improved PSO algorithm demonstrates superior optimization performance compared to standard methods.
The rolling training strategy significantly enhances the model's predictive accuracy.
Experimental results validate the model's strong adaptability and competitiveness in wind speed forecasting.

Conclusions:

The novel combined prediction model offers a robust solution for ultra-short-term wind speed forecasting.
The integration of VMD, PCA, spectral clustering, improved PSO, and rolling training yields enhanced prediction accuracy.
This approach provides a competitive and adaptable tool for improving wind power integration into electrical grids.