Multi-time scale wind speed prediction based on WT-bi-LSTM

Multi-time scale wind speed prediction based on WT-bi-LSTM

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...

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Bibliographic Details
Main Authors: Xiang Jinyong, Qiu Zhifeng, Hao Qihan, Cao Huhui
Format: Article
Language:English
Published: EDP Sciences 2020-01-01
Series:MATEC Web of Conferences
Subjects:
wind speed prediction
wavelet decomposition
bi-lstm
deep learning
Online Access:https://www.matec-conferences.org/articles/matecconf/pdf/2020/05/matecconf_cscns2020_05011.pdf
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spelling doaj-fc0030108c93494fa21951259315317d2021-08-05T13:49:42ZengEDP SciencesMATEC Web of Conferences2261-236X2020-01-013090501110.1051/matecconf/202030905011matecconf_cscns2020_05011Multi-time scale wind speed prediction based on WT-bi-LSTMXiang JinyongQiu ZhifengHao QihanCao HuhuiThe 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.https://www.matec-conferences.org/articles/matecconf/pdf/2020/05/matecconf_cscns2020_05011.pdfwind speed predictionwavelet decompositionbi-lstmdeep learning
collection DOAJ
language English
format Article
sources DOAJ
author Xiang Jinyong
Qiu Zhifeng
Hao Qihan
Cao Huhui
spellingShingle Xiang Jinyong
Qiu Zhifeng
Hao Qihan
Cao Huhui
Multi-time scale wind speed prediction based on WT-bi-LSTM
MATEC Web of Conferences
wind speed prediction
wavelet decomposition
bi-lstm
deep learning
author_facet Xiang Jinyong
Qiu Zhifeng
Hao Qihan
Cao Huhui
author_sort Xiang Jinyong
title Multi-time scale wind speed prediction based on WT-bi-LSTM
title_short Multi-time scale wind speed prediction based on WT-bi-LSTM
title_full Multi-time scale wind speed prediction based on WT-bi-LSTM
title_fullStr Multi-time scale wind speed prediction based on WT-bi-LSTM
title_full_unstemmed Multi-time scale wind speed prediction based on WT-bi-LSTM
title_sort multi-time scale wind speed prediction based on wt-bi-lstm
publisher EDP Sciences
series MATEC Web of Conferences
issn 2261-236X
publishDate 2020-01-01
description 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.
topic wind speed prediction
wavelet decomposition
bi-lstm
deep learning
url https://www.matec-conferences.org/articles/matecconf/pdf/2020/05/matecconf_cscns2020_05011.pdf
work_keys_str_mv AT xiangjinyong multitimescalewindspeedpredictionbasedonwtbilstm
AT qiuzhifeng multitimescalewindspeedpredictionbasedonwtbilstm
AT haoqihan multitimescalewindspeedpredictionbasedonwtbilstm
AT caohuhui multitimescalewindspeedpredictionbasedonwtbilstm
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