Conv‐ELSTM: An ensemble deep learning approach for predicting short‐term wind power
Abstract Accurate and reliable forecasting of wind power is essential for the stable integration of wind energy into the electrical grid. However, the chaotic nature of wind power presents a significant challenge in utilizing data for effective short‐term forecasting, such as 60‐min predictions. Thi...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
|
| Series: | IET Renewable Power Generation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rpg2.13159 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832579805854302208 |
|---|---|
| author | Guibin Wang Xinlong Huang Yiqun Li Hong Wang Xian Zhang Jing Qiu |
| author_facet | Guibin Wang Xinlong Huang Yiqun Li Hong Wang Xian Zhang Jing Qiu |
| author_sort | Guibin Wang |
| collection | DOAJ |
| description | Abstract Accurate and reliable forecasting of wind power is essential for the stable integration of wind energy into the electrical grid. However, the chaotic nature of wind power presents a significant challenge in utilizing data for effective short‐term forecasting, such as 60‐min predictions. This article introduces a hybrid data‐driven framework that employs an ensemble deep learning model to provide highly precise short‐term wind power predictions. The framework leverages a data‐driven approach to identify the intrinsic components of wind power data, including high‐frequency and low‐frequency components. A convolutional layer‐based feature fusion network is then established to properly extract important information from irrelevant wind energy features. Subsequently, an ensemble of long short‐term memory (LSTM) networks is developed to forecast wind power using the fused features, thereby mitigating the disadvantage of a single prediction model. The numerical experiment is carried out based on two different real‐life datasets. The results demonstrate the effectiveness of the proposed method in forecasting short‐term wind power compared to five benchmarks. |
| format | Article |
| id | doaj-art-1243d74b881f4e8f9776d0471c50bfed |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-1243d74b881f4e8f9776d0471c50bfed2025-01-30T12:15:54ZengWileyIET Renewable Power Generation1752-14161752-14242024-12-0118164084409610.1049/rpg2.13159Conv‐ELSTM: An ensemble deep learning approach for predicting short‐term wind powerGuibin Wang0Xinlong Huang1Yiqun Li2Hong Wang3Xian Zhang4Jing Qiu5College of Mechatronics and Control Engineering Shenzhen University Shenzhen ChinaCollege of Mechatronics and Control Engineering Shenzhen University Shenzhen ChinaSchool of Transportation Southeast University Nanjing ChinaElectric Power Research Institute China Southern Grid Guangzhou ChinaSchool of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen ChinaSchool of Electrical and Information Engineering The University of Sydney Sydney AustraliaAbstract Accurate and reliable forecasting of wind power is essential for the stable integration of wind energy into the electrical grid. However, the chaotic nature of wind power presents a significant challenge in utilizing data for effective short‐term forecasting, such as 60‐min predictions. This article introduces a hybrid data‐driven framework that employs an ensemble deep learning model to provide highly precise short‐term wind power predictions. The framework leverages a data‐driven approach to identify the intrinsic components of wind power data, including high‐frequency and low‐frequency components. A convolutional layer‐based feature fusion network is then established to properly extract important information from irrelevant wind energy features. Subsequently, an ensemble of long short‐term memory (LSTM) networks is developed to forecast wind power using the fused features, thereby mitigating the disadvantage of a single prediction model. The numerical experiment is carried out based on two different real‐life datasets. The results demonstrate the effectiveness of the proposed method in forecasting short‐term wind power compared to five benchmarks.https://doi.org/10.1049/rpg2.13159wind powerwind power plants |
| spellingShingle | Guibin Wang Xinlong Huang Yiqun Li Hong Wang Xian Zhang Jing Qiu Conv‐ELSTM: An ensemble deep learning approach for predicting short‐term wind power IET Renewable Power Generation wind power wind power plants |
| title | Conv‐ELSTM: An ensemble deep learning approach for predicting short‐term wind power |
| title_full | Conv‐ELSTM: An ensemble deep learning approach for predicting short‐term wind power |
| title_fullStr | Conv‐ELSTM: An ensemble deep learning approach for predicting short‐term wind power |
| title_full_unstemmed | Conv‐ELSTM: An ensemble deep learning approach for predicting short‐term wind power |
| title_short | Conv‐ELSTM: An ensemble deep learning approach for predicting short‐term wind power |
| title_sort | conv elstm an ensemble deep learning approach for predicting short term wind power |
| topic | wind power wind power plants |
| url | https://doi.org/10.1049/rpg2.13159 |
| work_keys_str_mv | AT guibinwang convelstmanensembledeeplearningapproachforpredictingshorttermwindpower AT xinlonghuang convelstmanensembledeeplearningapproachforpredictingshorttermwindpower AT yiqunli convelstmanensembledeeplearningapproachforpredictingshorttermwindpower AT hongwang convelstmanensembledeeplearningapproachforpredictingshorttermwindpower AT xianzhang convelstmanensembledeeplearningapproachforpredictingshorttermwindpower AT jingqiu convelstmanensembledeeplearningapproachforpredictingshorttermwindpower |