Prediction of Wind Farm Power Output Based on an Enhanced Recurrent Neural Network - Eze, Ethelbert Chinedu; E.S. Ibrahim; C.K. Ihim; C.R Chatwin; T. C. Yang (IJRDO -Journal of Computer Science Engineering, 2019)
This paper studies wind-farm power output prediction based on recurrent neural network. First, a hybrid recurrent neural network (RNN) regularization method involving dropout and long short-term memory (LSTM) is presented. In this model, a regularization scheme is applied to modify and adapt the stochastic nature of the wind. Secondly, a new data structure is presented to the model. Thirdly, the method is developed for wind farm power output (WFPO) prediction. This algorithm is based on the dropout method, which has made WFPO capable of better prediction irrespective of the non-deterministic wind speed. The LSTM solves the RNN limitation of overfitting. The proposed method is demonstrated by investigating the WFPO on a fourteen wind-turbines, provides up to 80% accurate result over ARIMA model.