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In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
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A robust chaos-inspired artificial intelligence model for dealing with nonlinear dynamics in wind speed forecasting.

Caner Barış1, Cağfer Yanarateş2, Aytaç Altan1

  • 1Department of Electrical and Electronics Engineering, Zonguldak Bülent Ecevit University, Zonguldak, Turkey.

Peerj. Computer Science
|December 9, 2024
PubMed
Summary

Accurate wind speed forecasting is vital for efficient wind energy integration. This study developed a hybrid model using robust empirical mode decomposition and a long short-term memory network optimized by the African vultures algorithm for improved renewable energy generation.

Keywords:
African vultures optimization algorithmLong short-term memoryRobust signal decompositionTent chaotic mappingWind speed forecasting

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Area of Science:

  • Renewable Energy Systems
  • Climate Change Mitigation
  • Artificial Intelligence in Energy

Background:

  • Climate change, driven by fossil fuels, necessitates renewable energy adoption.
  • Wind energy is a key renewable source, but its integration is challenged by wind speed variability.
  • Accurate wind speed forecasting is crucial for optimizing wind energy efficiency and grid stability.

Purpose of the Study:

  • To develop a robust wind speed forecasting model to handle non-linear dynamics.
  • To improve the accuracy and efficiency of wind energy generation.
  • To evaluate a novel hybrid deep learning approach for wind speed prediction.

Main Methods:

  • Wind speed data from Bandırma, Turkey, was decomposed using Robust Empirical Mode Decomposition (REMD).
  • Intrinsic Mode Functions (IMFs) were processed by a Long Short-Term Memory (LSTM) network.
  • Model parameters were optimized using the African Vultures Optimization (AVO) algorithm with tent chaotic mapping, compared against Chaotic Particle Swarm Optimization (CPSO).

Main Results:

  • The proposed hybrid model demonstrated high accuracy in wind speed forecasting.
  • The African Vultures Optimization algorithm effectively improved LSTM model parameters compared to CPSO.
  • The study validated the effectiveness of advanced optimization and deep learning for wind energy applications.

Conclusions:

  • Advanced optimization techniques and deep learning models significantly enhance wind speed forecasting accuracy.
  • The developed hybrid model offers a robust solution for efficient and sustainable wind energy generation.
  • This research contributes to overcoming challenges in wind energy integration and maximizing its potential.