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.
Highlights
In the field of renewable energy power generation, wind power resource is the most popular one due to their advantages of cleanness, wide distribution and easy to be utilized on large scales, and has attracted attention of all countries[1]
In order to obtain higher accuracy of wind speed prediction, a neural network model based on bidirectional long-short memory is proposed to extract historical and future information of wind speed after the signal is transformed by wavelet
In the model proposed in this paper, the hidden state Ht of the bi-long short term memory (LSTM) network is passed to a fully connected layer to complete the non-linear mapping of the intermediate state; and a regression layer is used to realize the regression prediction of the wind speed signal
Summary
In the field of renewable energy power generation, wind power resource is the most popular one due to their advantages of cleanness, wide distribution and easy to be utilized on large scales, and has attracted attention of all countries[1]. Long short term memory (LSTM) neural network model can make full use of the information of continuous samples, especially the information with long-term dependence. It is often used in time series problems such as speech recognition, machine translation, handwriting recognition and so on. In order to obtain higher accuracy of wind speed prediction, a neural network model based on bidirectional long-short memory (bi-LSTM) is proposed to extract historical and future information of wind speed after the signal is transformed by wavelet. The proposed model in this paper which considers bidirectional information has the highest prediction precision and accuracy, especially in extracting trend information of wind speed in large time scale.
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