A Novel Hybrid Model for Short-Term Wind Speed Forecasting Based on Twice Decomposition, PSR, and IMVO-ELM
Accurate wind speed forecasting is an effective way to improve the safety and stability of power grid. A novel hybrid model based on twice decomposition, phase space reconstruction (PSR), and an improved multiverse optimizer-extreme learning machine (IMVO-ELM) is proposed to enhance the performance...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/4014048 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832567917469761536 |
|---|---|
| author | Xin Xia Xiaolu Wang |
| author_facet | Xin Xia Xiaolu Wang |
| author_sort | Xin Xia |
| collection | DOAJ |
| description | Accurate wind speed forecasting is an effective way to improve the safety and stability of power grid. A novel hybrid model based on twice decomposition, phase space reconstruction (PSR), and an improved multiverse optimizer-extreme learning machine (IMVO-ELM) is proposed to enhance the performance of short-term wind speed forecasting in this paper. In consideration of the nonstationarity of the wind speed signal, a twice decomposition based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), fuzzy entropy, and variational mode decomposition (VMD) is proposed to reduce the nonstationarity of the original signal firstly. Then the PSR based on C-C method is employed to reconstitute the decomposed signal as the input of the prediction model. Lastly, an improved multiverse optimizer is proposed to improve the stability and efficiency of ELM which is used as prediction model. Furthermore, two experiments are designed to verify the performance of the proposed method; the results indicate that (1) the wind speed forecasting with twice decomposition of original wind speed signal is better than other once-decomposition methods and much better than forecasting without decomposition; (2) the C-C-PSR method can determine the input dimension of ELM and improve the prediction accuracy of ELM; (3) the IMVO has improved the stability of ELM, and the optimization efficiency is better than other comparison optimization methods. The results show that the proposed hybrid approach is a useful tool for short-term wind speed forecasting. |
| format | Article |
| id | doaj-art-1b28fc3e51c0422082da0ec921bfbd21 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-1b28fc3e51c0422082da0ec921bfbd212025-02-03T01:00:13ZengWileyComplexity1099-05262022-01-01202210.1155/2022/4014048A Novel Hybrid Model for Short-Term Wind Speed Forecasting Based on Twice Decomposition, PSR, and IMVO-ELMXin Xia0Xiaolu Wang1School of Mechanical and Electrical EngineeringSchool of Mechanical and Electrical EngineeringAccurate wind speed forecasting is an effective way to improve the safety and stability of power grid. A novel hybrid model based on twice decomposition, phase space reconstruction (PSR), and an improved multiverse optimizer-extreme learning machine (IMVO-ELM) is proposed to enhance the performance of short-term wind speed forecasting in this paper. In consideration of the nonstationarity of the wind speed signal, a twice decomposition based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), fuzzy entropy, and variational mode decomposition (VMD) is proposed to reduce the nonstationarity of the original signal firstly. Then the PSR based on C-C method is employed to reconstitute the decomposed signal as the input of the prediction model. Lastly, an improved multiverse optimizer is proposed to improve the stability and efficiency of ELM which is used as prediction model. Furthermore, two experiments are designed to verify the performance of the proposed method; the results indicate that (1) the wind speed forecasting with twice decomposition of original wind speed signal is better than other once-decomposition methods and much better than forecasting without decomposition; (2) the C-C-PSR method can determine the input dimension of ELM and improve the prediction accuracy of ELM; (3) the IMVO has improved the stability of ELM, and the optimization efficiency is better than other comparison optimization methods. The results show that the proposed hybrid approach is a useful tool for short-term wind speed forecasting.http://dx.doi.org/10.1155/2022/4014048 |
| spellingShingle | Xin Xia Xiaolu Wang A Novel Hybrid Model for Short-Term Wind Speed Forecasting Based on Twice Decomposition, PSR, and IMVO-ELM Complexity |
| title | A Novel Hybrid Model for Short-Term Wind Speed Forecasting Based on Twice Decomposition, PSR, and IMVO-ELM |
| title_full | A Novel Hybrid Model for Short-Term Wind Speed Forecasting Based on Twice Decomposition, PSR, and IMVO-ELM |
| title_fullStr | A Novel Hybrid Model for Short-Term Wind Speed Forecasting Based on Twice Decomposition, PSR, and IMVO-ELM |
| title_full_unstemmed | A Novel Hybrid Model for Short-Term Wind Speed Forecasting Based on Twice Decomposition, PSR, and IMVO-ELM |
| title_short | A Novel Hybrid Model for Short-Term Wind Speed Forecasting Based on Twice Decomposition, PSR, and IMVO-ELM |
| title_sort | novel hybrid model for short term wind speed forecasting based on twice decomposition psr and imvo elm |
| url | http://dx.doi.org/10.1155/2022/4014048 |
| work_keys_str_mv | AT xinxia anovelhybridmodelforshorttermwindspeedforecastingbasedontwicedecompositionpsrandimvoelm AT xiaoluwang anovelhybridmodelforshorttermwindspeedforecastingbasedontwicedecompositionpsrandimvoelm AT xinxia novelhybridmodelforshorttermwindspeedforecastingbasedontwicedecompositionpsrandimvoelm AT xiaoluwang novelhybridmodelforshorttermwindspeedforecastingbasedontwicedecompositionpsrandimvoelm |