A combined model based on secondary decomposition technique and grey wolf optimizer for short-term wind power forecasting
Short-term wind power forecasting plays an important role in wind power generation systems. In order to improve the accuracy of wind power forecasting, many researchers have proposed a large number of wind power forecasting models. However, traditional forecasting models ignore data preprocessing an...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-02-01
|
| Series: | Frontiers in Energy Research |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1078751/full |
| _version_ | 1797935791077326848 |
|---|---|
| author | Zhongde Su Bowen Zheng Huacai Lu |
| author_facet | Zhongde Su Bowen Zheng Huacai Lu |
| author_sort | Zhongde Su |
| collection | DOAJ |
| description | Short-term wind power forecasting plays an important role in wind power generation systems. In order to improve the accuracy of wind power forecasting, many researchers have proposed a large number of wind power forecasting models. However, traditional forecasting models ignore data preprocessing and the limitations of a single forecasting model, resulting in low forecasting accuracy. Aiming at the shortcomings of the existing models, a combined forecasting model based on secondary decomposition technique and grey wolf optimizer (GWO) is proposed. In the process of forecasting, firstly, the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and wavelet transform (WT) are used to preprocess the wind power data. Then, least squares support vector machine (LSSVM), extreme learning machine (ELM) and back propagation neural network (BPNN) are established to forecast the decomposed components respectively. In order to improve the forecasting performance, the parameters in LSSVM, ELM, and BPNN are tuned by GWO. Finally, the GWO is used to determine the weight coefficient of each single forecasting model, and the weighted combination is used to obtain the final forecasting result. The simulation results show that the forecasting model has better forecasting performance than other forecasting models. |
| first_indexed | 2024-04-10T18:19:41Z |
| format | Article |
| id | doaj.art-fb2015a6391c49ae81dd61c8219133dd |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-10T18:19:41Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-fb2015a6391c49ae81dd61c8219133dd2023-02-02T07:34:09ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-02-011110.3389/fenrg.2023.10787511078751A combined model based on secondary decomposition technique and grey wolf optimizer for short-term wind power forecastingZhongde SuBowen ZhengHuacai LuShort-term wind power forecasting plays an important role in wind power generation systems. In order to improve the accuracy of wind power forecasting, many researchers have proposed a large number of wind power forecasting models. However, traditional forecasting models ignore data preprocessing and the limitations of a single forecasting model, resulting in low forecasting accuracy. Aiming at the shortcomings of the existing models, a combined forecasting model based on secondary decomposition technique and grey wolf optimizer (GWO) is proposed. In the process of forecasting, firstly, the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and wavelet transform (WT) are used to preprocess the wind power data. Then, least squares support vector machine (LSSVM), extreme learning machine (ELM) and back propagation neural network (BPNN) are established to forecast the decomposed components respectively. In order to improve the forecasting performance, the parameters in LSSVM, ELM, and BPNN are tuned by GWO. Finally, the GWO is used to determine the weight coefficient of each single forecasting model, and the weighted combination is used to obtain the final forecasting result. The simulation results show that the forecasting model has better forecasting performance than other forecasting models.https://www.frontiersin.org/articles/10.3389/fenrg.2023.1078751/fullwind power forecastingsecondary decomposition techniquemachine learningcombined modelgrey wolf optimizer |
| spellingShingle | Zhongde Su Bowen Zheng Huacai Lu A combined model based on secondary decomposition technique and grey wolf optimizer for short-term wind power forecasting Frontiers in Energy Research wind power forecasting secondary decomposition technique machine learning combined model grey wolf optimizer |
| title | A combined model based on secondary decomposition technique and grey wolf optimizer for short-term wind power forecasting |
| title_full | A combined model based on secondary decomposition technique and grey wolf optimizer for short-term wind power forecasting |
| title_fullStr | A combined model based on secondary decomposition technique and grey wolf optimizer for short-term wind power forecasting |
| title_full_unstemmed | A combined model based on secondary decomposition technique and grey wolf optimizer for short-term wind power forecasting |
| title_short | A combined model based on secondary decomposition technique and grey wolf optimizer for short-term wind power forecasting |
| title_sort | combined model based on secondary decomposition technique and grey wolf optimizer for short term wind power forecasting |
| topic | wind power forecasting secondary decomposition technique machine learning combined model grey wolf optimizer |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2023.1078751/full |
| work_keys_str_mv | AT zhongdesu acombinedmodelbasedonsecondarydecompositiontechniqueandgreywolfoptimizerforshorttermwindpowerforecasting AT bowenzheng acombinedmodelbasedonsecondarydecompositiontechniqueandgreywolfoptimizerforshorttermwindpowerforecasting AT huacailu acombinedmodelbasedonsecondarydecompositiontechniqueandgreywolfoptimizerforshorttermwindpowerforecasting AT zhongdesu combinedmodelbasedonsecondarydecompositiontechniqueandgreywolfoptimizerforshorttermwindpowerforecasting AT bowenzheng combinedmodelbasedonsecondarydecompositiontechniqueandgreywolfoptimizerforshorttermwindpowerforecasting AT huacailu combinedmodelbasedonsecondarydecompositiontechniqueandgreywolfoptimizerforshorttermwindpowerforecasting |