Author:
Xiang Jinyong,Qiu Zhifeng,Hao Qihan,Cao Huhui
Abstract
The accurate and reliable wind speed prediction can benefit the wind power forecasting and its consumption. As a continuous signal with the high autocorrelation, wind speed is closely related to the past and future moments. Therefore, to fully use the information of two direction, an auto-regression model based on the bi-directional long short term memory neural network model with wavelet decomposition (WT-bi-LSTM) is built to predict the wind speed at multi-time scales. The proposed model are validated by using the actual wind speed series from a wind farm in China. The validation results demonstrated that, compared with other four traditional models, the proposed strategy can effectively improve the accuracy of wind speed prediction.
Reference19 articles.
1. Hui L, Mi X, Li Y. Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM [J]. Energy Conversion & Management, 2018, 159(54-64.
2. Khoddayar M, Wang J. Spatio-temporal Graph Deep Neural Network for Short-term Wind Speed Forecasting [J]. IEEE Transactions on Sustainable Energy, 2018, PP(99): 1-.
3. Yu C, Li Y, Zhang M. Comparative study on three new hybrid models using Elman Neural Network and Empirical Mode Decomposition based technologies improved by Singular Spectrum Analysis for hour-ahead wind speed forecasting [J]. Energy Conversion and Management, 2017, 147(75-85.
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