Prediction of ultra-short-term wind power based on CEEMDAN-LSTM-TCN
So as to decrease those cacoethic impact of a huge amount of wind energy generation systems associated with the electric power system and improve the utilization rate and the budgetary profits of wind power era, this paper raises a neural network in view of CEEMDAN-LSTM-TCN. Firstly, CEEMDAN is used...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722018959 |
| _version_ | 1797950771810009088 |
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| author | Chenjia Hu Yan Zhao He Jiang Mingkun Jiang Fucai You Qian Liu |
| author_facet | Chenjia Hu Yan Zhao He Jiang Mingkun Jiang Fucai You Qian Liu |
| author_sort | Chenjia Hu |
| collection | DOAJ |
| description | So as to decrease those cacoethic impact of a huge amount of wind energy generation systems associated with the electric power system and improve the utilization rate and the budgetary profits of wind power era, this paper raises a neural network in view of CEEMDAN-LSTM-TCN. Firstly, CEEMDAN is used to break down the wind velocity arrangement to decrease the sway of arbitrariness Furthermore variance about wind velocity. Secondly, the ultra-short-term wind power forecast depend upon LSTM and TCN is built to realize the real-time prediction for wind energy. Finally, the simulation results show that LSTM-TCN can deal with multi time order characteristics and predict ultra-short period wind energy with effect, which is better than LSTM and TCN. It also has a scientific reference for local power dispatching. |
| first_indexed | 2024-04-10T22:20:26Z |
| format | Article |
| id | doaj.art-fd38836cfd724779af72da508d03c5d4 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T22:20:26Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-fd38836cfd724779af72da508d03c5d42023-01-18T04:31:44ZengElsevierEnergy Reports2352-48472022-11-018483492Prediction of ultra-short-term wind power based on CEEMDAN-LSTM-TCNChenjia Hu0Yan Zhao1He Jiang2Mingkun Jiang3Fucai You4Qian Liu5Shenyang Institute of Engineering, Shenyang 110136, China; Key Laboratory of Regional Multi-energy System Integration and Control of Liaoning Province, Shenyang 110136, ChinaShenyang Institute of Engineering, Shenyang 110136, China; Key Laboratory of Regional Multi-energy System Integration and Control of Liaoning Province, Shenyang 110136, China; Corresponding author at: Shenyang Institute of Engineering, Shenyang 110136, China.Shenyang Institute of Engineering, Shenyang 110136, China; Key Laboratory of Regional Multi-energy System Integration and Control of Liaoning Province, Shenyang 110136, ChinaShenyang Institute of Engineering, Shenyang 110136, China; Key Laboratory of Regional Multi-energy System Integration and Control of Liaoning Province, Shenyang 110136, ChinaShenyang Institute of Engineering, Shenyang 110136, China; Key Laboratory of Regional Multi-energy System Integration and Control of Liaoning Province, Shenyang 110136, ChinaLiaoning Province Information Centre, Shenyang 110002, ChinaSo as to decrease those cacoethic impact of a huge amount of wind energy generation systems associated with the electric power system and improve the utilization rate and the budgetary profits of wind power era, this paper raises a neural network in view of CEEMDAN-LSTM-TCN. Firstly, CEEMDAN is used to break down the wind velocity arrangement to decrease the sway of arbitrariness Furthermore variance about wind velocity. Secondly, the ultra-short-term wind power forecast depend upon LSTM and TCN is built to realize the real-time prediction for wind energy. Finally, the simulation results show that LSTM-TCN can deal with multi time order characteristics and predict ultra-short period wind energy with effect, which is better than LSTM and TCN. It also has a scientific reference for local power dispatching.http://www.sciencedirect.com/science/article/pii/S2352484722018959Wind power generation systemWind powerRenewable energyUltra short-term wind power forecast |
| spellingShingle | Chenjia Hu Yan Zhao He Jiang Mingkun Jiang Fucai You Qian Liu Prediction of ultra-short-term wind power based on CEEMDAN-LSTM-TCN Energy Reports Wind power generation system Wind power Renewable energy Ultra short-term wind power forecast |
| title | Prediction of ultra-short-term wind power based on CEEMDAN-LSTM-TCN |
| title_full | Prediction of ultra-short-term wind power based on CEEMDAN-LSTM-TCN |
| title_fullStr | Prediction of ultra-short-term wind power based on CEEMDAN-LSTM-TCN |
| title_full_unstemmed | Prediction of ultra-short-term wind power based on CEEMDAN-LSTM-TCN |
| title_short | Prediction of ultra-short-term wind power based on CEEMDAN-LSTM-TCN |
| title_sort | prediction of ultra short term wind power based on ceemdan lstm tcn |
| topic | Wind power generation system Wind power Renewable energy Ultra short-term wind power forecast |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722018959 |
| work_keys_str_mv | AT chenjiahu predictionofultrashorttermwindpowerbasedonceemdanlstmtcn AT yanzhao predictionofultrashorttermwindpowerbasedonceemdanlstmtcn AT hejiang predictionofultrashorttermwindpowerbasedonceemdanlstmtcn AT mingkunjiang predictionofultrashorttermwindpowerbasedonceemdanlstmtcn AT fucaiyou predictionofultrashorttermwindpowerbasedonceemdanlstmtcn AT qianliu predictionofultrashorttermwindpowerbasedonceemdanlstmtcn |