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Updated: Sep 17, 2025

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Summary
This summary is machine-generated.

This study enhances wind energy assessment and prediction using the Weibull model and a hybrid approach. The improved wolf pack algorithm and ARIMA model accurately model wind speed, boosting renewable energy development.

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Area of Science:

  • Renewable Energy Systems
  • Computational Intelligence
  • Statistical Modeling

Background:

  • Wind energy is a vital renewable source, but its intermittency poses challenges for efficient development and accurate resource assessment.
  • Predicting wind speed is crucial for optimizing wind energy utilization and reducing development costs.

Purpose of the Study:

  • To develop an accurate wind speed prediction and resource assessment method for wind energy.
  • To improve the efficiency and reliability of wind energy development and utilization.

Main Methods:

  • Utilized the Weibull model for wind speed data fitting.
  • Introduced an improved wolf pack algorithm (via pollination mechanism) for resource assessment.
  • Employed data decomposition, Autoregressive Moving Average (ARIMA), and Cuckoo Search for hybrid wind speed prediction.

Main Results:

  • The Weibull model demonstrated high fitting accuracy (R-squared 0.96).
  • The hybrid prediction model achieved high accuracy, with deviations under 3% and significantly outperforming VMD-ISOA-KELM and CNN-BLSTM.
  • The proposed method showed low time complexity (under 5 seconds) and high operational efficiency.

Conclusions:

  • The developed Weibull model and hybrid prediction approach effectively assess wind energy resources and predict wind speed.
  • The research provides a robust technical foundation for advancing the wind energy industry and promoting sustainable development.
  • The method offers improved accuracy and efficiency compared to existing models, addressing key challenges in wind energy utilization.