Abstract
Wind energy is considered as one of the most attractive renewable energies and has the fastest development in power fields. It gradually becomes an indispensable source for society, especially for generating electricity. Due to the fluctuation of this energy, forecasting wind speed is necessary to adjust wind power accordingly. In this paper, a hybrid model using a Bidirectional Long-Short Term Memory network (Bi-LSTM) with the Complete Ensemble Empirical Mode Decomposition method (CEEMD) to make a multi-step forecast for wind speed in Vietnam was proposed. Initially, the CEEMD method was used to decompose original historical data into a set of constitutive series. Then, the decomposed components were the inputs for the Bi-LSTM model to make a prediction. Bi-LSTM network could utilize the information in both forward and backward directions completely. The performances of the proposed model were compared with that of other models. The final results demonstrated that the forecasting accuracy of the proposed model was higher than the comparative methods.
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This research is funded by Hanoi University of Science and Technology (HUST) under grant number T2021-PC-004.
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Thu, N.T.H., Van, P.N., Bao, P.Q. (2023). Multi-step Ahead Wind Speed Forecasting Based on a Bi-LSTM Network Combined with Decomposition Technique. In: Huang, YP., Wang, WJ., Quoc, H.A., Le, HG., Quach, HN. (eds) Computational Intelligence Methods for Green Technology and Sustainable Development. GTSD 2022. Lecture Notes in Networks and Systems, vol 567. Springer, Cham. https://doi.org/10.1007/978-3-031-19694-2_50
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