Near real-time wind speed forecast model with bidirectional LSTM networks

Abstract

Wind is an important source of renewable energy, often used to provide clean electricity to remote areas. For optimal extraction of this energy source, there is a need for an accurate and robust wind speed forecasting. The intermittent nature of wind makes this goal quite challenging. This research proposes a novel hybrid bidirectional LSTM (BiLSTM) model for near real-time wind speed forecasting. The hybrid model is developed using wind speed and selected climate indices from a group of neighbouring reference stations as predictors to forecast wind speed of a target station. A 3-stage feature selection is applied on the predictors to robustly extract highly significant input features. Stage 1 employs partial auto-correlation and cross-correlation, stage 2 uses the RReliefF filter algorithm, and Boruta-RF wrapper method is implemented in the final stage to improve the BiLSTM model with an efficient Bayesian optimization used for hyperparameter tuning. The proposed model has been benchmarked with comparative models including standalone and hybrid LSTM, RNN, MLP and RF. The proposed hybrid BiLSTM algorithm is found to be superior in wind speed prediction for all tested sites with ≈76.6−84.8% of errors being ≤|0.5|ms−1. The hybrid BiLSTM model also registered the lowest Relative Root Mean Square Error (9.6−23.8%) and Mean Absolute Percentage Error (8.8−21.5%) among all the tested algorithms. This research ascertains that the proposed model can accurately predict wind speed and capacitate wind energy availability to be regularly monitored at a near real-time level.