Application of stacked and bidirectional long short-term memory deep learning models for wind speed forecasting at an offshore site

Abstract

ABSTRACT Very short-term offshore wind speed forecasting by application of Stacked long short-term memory (LSTM) and Bidirectional LSTM deep learning models is done in this work. Wind speed data of two different offshore sites located in two different continents are used for testing the models. Performance is measured on the basis of accuracy of forecasting and computational time. The effectiveness of Stacked LSTM and Bidirectional LSTM models is also validated by comparing their performance with convolutional neural network, convolutional neural network-long short-term memory network, multi-layer perceptron, and rolling forecasting auto regressive integrated moving average models. Results of forecasting error confirm that Stacked LSTM model is better than other compared models in forecasting very short-term offshore wind speed. Mean absolute percentage error (MAPE) of wind speed forecasting by Stacked LSTM model is 4.59% at Anholt (Denmark) and 3.62% at Dhanushkodi (India) sites. From comparison of MAPE of Stacked LSTM model with that of eight other latest existing models in literature, it can be concluded that Stacked LSTM model is superior to many other existing models.