Hybrid Deep Learning Model for Ultra-Short-Term Wind Power Forecasting

Abstract

Predicting the output of renewable energy sources has an important role in minimizing operational cost, improving reliability and security of a power system. However, accurate prediction is challenging due to the high non-linear power generation. This paper develops a hybrid deep learning algorithm to forecast accurately the ultra-short-term wind power generation of Boco Rock Wind Farm in Australia. The hybrid algorithm consists of gated recurrent units (GRU), long short-term memory (LSTM) and fully connected neural network. The effectiveness of the proposed model is evaluated against other advanced models, including multilayer neural network (NN), bidirectional LSTM, recurrent neural network (RNN), GRU and LSTM. It is found that the forecasting model demonstrates 12.47% higher accuracy in MAE, 38.77% in MAPE and 10.97% in RMSE as compared to a NN.No Full Tex