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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2020-01-01
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| Series: | MATEC Web of Conferences |
| Subjects: | |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2020/05/matecconf_cscns2020_05011.pdf |
| _version_ | 1828914676684029952 |
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| author | Xiang Jinyong Qiu Zhifeng Hao Qihan Cao Huhui |
| author_facet | Xiang Jinyong Qiu Zhifeng Hao Qihan Cao Huhui |
| author_sort | Xiang Jinyong |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-13T20:00:03Z |
| format | Article |
| id | doaj.art-fc0030108c93494fa21951259315317d |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-12-13T20:00:03Z |
| publishDate | 2020-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-fc0030108c93494fa21951259315317d2022-12-21T23:33:13ZengEDP 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 |
| 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 |
| title | 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_short | Multi-time scale wind speed prediction based on WT-bi-LSTM |
| title_sort | multi time scale wind speed prediction based on wt bi lstm |
| 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 |