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Related Concept Videos

Wind Turbine Machine Models01:24

Wind Turbine Machine Models

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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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Related Experiment Videos

An Intelligent Ensemble Neural Network Model for Wind Speed Prediction in Renewable Energy Systems.

V Ranganayaki1, S N Deepa1

  • 1Department of Electrical and Electronics Engineering, Anna University, Regional Campus Coimbatore, Coimbatore, Tamil Nadu 641 046, India.

Thescientificworldjournal
|April 2, 2016
PubMed
Summary
This summary is machine-generated.

This study proposes a novel ensemble neural network model to accurately predict wind speed for renewable energy applications. By averaging predictions from multiple neural networks and using 102 criteria to optimize hidden neurons, the model minimizes errors and avoids overfitting.

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Area of Science:

  • Artificial Intelligence
  • Renewable Energy Systems
  • Computational Neuroscience

Background:

  • Accurate wind speed prediction is crucial for efficient renewable energy integration.
  • Artificial Neural Networks (ANNs) are widely used for time-series forecasting, but selecting the optimal number of hidden neurons remains a challenge.
  • Suboptimal hidden neuron selection in ANNs can lead to overfitting or underfitting, compromising prediction accuracy.

Purpose of the Study:

  • To propose a novel intelligent ensemble neural network (ENN) model for enhanced wind speed prediction.
  • To introduce and validate 102 criteria for selecting the optimal number of hidden neurons in ANNs, mitigating overfitting and underfitting.
  • To improve the accuracy and reduce the error in wind speed forecasting for renewable energy applications.

Main Methods:

  • An ensemble neural network model was developed by averaging forecasts from Multilayer Perceptron (MLP), Madaline, Back Propagation Neural Network (BPN), and Probabilistic Neural Network (PNN).
  • 102 distinct criteria were employed to determine the optimal number of hidden neurons, validated using convergence theorems and error metrics.
  • The proposed ENN model was applied to real-time wind data for performance evaluation.

Main Results:

  • The proposed ensemble model significantly reduced prediction errors compared to individual neural network models.
  • The 102 criteria effectively addressed overfitting and underfitting issues in ANN model development.
  • Simulation results demonstrated enhanced accuracy and minimized error in wind speed prediction using the ENN model.

Conclusions:

  • The developed intelligent ensemble neural network model offers a robust and accurate solution for wind speed forecasting.
  • The proposed criteria for hidden neuron selection provide a reliable method for optimizing ANN performance.
  • The ENN model proves effective in improving the accuracy of wind speed prediction for renewable energy applications.