Ultra-short term wind speed prediction using mathematical morphology decomposition and long short-term memory

This paper proposes a new model, which consists of a mathematical morphology (MM) decomposer and two long short term memory (LSTM) networks, to perform ultra-short term wind speed forecast. The MM decomposer is developed in order to improve the forecast accuracy, which separates the wind speed into two parts: a stationary long-term baseline and a nonstationary short-term residue. Afterwards, two LSTM networks are implemented to forecast the baseline and residue, respectively. Besides, this paper makes an integrated forecast that takes into account multiple climate factors, such as temperature and air pressure. The baseline, temperature and air pressure are used as the inputs of baseline network for training and prediction, and the baseline, residue, temperature and air pressure are used as the inputs of residue network for training and prediction. The performance of the proposed model has been validated using data collected from the Australian Meteorological Station, which is compared with least squares-support vector machine (LS-SVM), back-propagation artificial neural network (BPNN), LSTM, MM-LS-SVM, and MM-BPNN. The results demonstrate that the proposed model is more suitable to solve non-stationary time-series forecast, and achieves higher accuracy than the other models under various conditions.