Enhanced Recurrent Neural Network for Short-term Wind Farm Power Output Prediction.

  • Ethelbert Chinedu Eze University of Sussex
  • Chris R. Chatwin, Professor.
Keywords: Wind Power Output Prediction, Recurrent Neural Network, Deep learning, oLSTM, ARIMA, MSE


Scientists, investors and policy makers have become aware of the importance of providing near accurate spatial estimates of renewable energies. This is why current studies show improvements in methodologies to provide more precise energy predictions. Wind energy is tied to weather patterns, which are irregular, especially in climates with erratic weather patterns.  This can lead to errors in the predicted potentials. Therefore, recurrent neural networks (RNN) are exploited for enhanced wind-farm power output prediction. A model involving a combination of RNN regularization methods using dropout and long short-term memory (LSTM) is presented. In this model, the regularization scheme modifies and adapts to the stochastic nature of wind and is optimised for the wind farm power output (WFPO) prediction. This algorithm implements a dropout method to suit non-deterministic wind speed by applying LSTM to prevent RNN from overfitting. A demonstration for accuracy using the proposed method is performed on a 14-turbines wind farm. The model out performs the ARIMA model with up to 80% accuracy.


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Author Biography

Ethelbert Chinedu Eze, University of Sussex

I'm a PhD Candidate in Informatics at the University of Sussex, U.K.


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