Nonparametric Probabilistic Prediction of Ultra-Short-Term Wind Power Based on MultiFusion–ChronoNet–AMC
Accurate forecasting is crucial for enhancing the flexibility and controllability of power grids. Traditional forecasting methods mainly focus on modeling based on a single data source, which leads to an inability to fully capture the underlying relationships in wind power data. In addition, current...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/7/1646 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849730301579755520 |
|---|---|
| author | Yan Yan Yong Qian Yan Zhou |
| author_facet | Yan Yan Yong Qian Yan Zhou |
| author_sort | Yan Yan |
| collection | DOAJ |
| description | Accurate forecasting is crucial for enhancing the flexibility and controllability of power grids. Traditional forecasting methods mainly focus on modeling based on a single data source, which leads to an inability to fully capture the underlying relationships in wind power data. In addition, current models often lack dynamic adaptability to data characteristics, resulting in lower prediction accuracy and reliability under different time periods or weather conditions. To address the aforementioned issues, an ultra-short-term hybrid probabilistic prediction model based on MultiFusion, ChronoNet, and adaptive Monte Carlo (AMC) is proposed in this paper. By combining multi-source data fusion and a multiple-gated structure, the nonlinear characteristics and uncertainties of wind power under various input conditions are effectively captured by this model. Additionally, the AMC method is applied in this paper to provide comprehensive, accurate, and flexible ultra-short-term probabilistic predictions. Ultimately, experiments are conducted on multiple datasets, and the results show that the proposed model not only improves the accuracy of deterministic prediction but also enhances the reliability of probabilistic prediction intervals. |
| format | Article |
| id | doaj-art-089cc6b972b8499d829d0b7e8da275da |
| institution | DOAJ |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-089cc6b972b8499d829d0b7e8da275da2025-08-20T03:08:55ZengMDPI AGEnergies1996-10732025-03-01187164610.3390/en18071646Nonparametric Probabilistic Prediction of Ultra-Short-Term Wind Power Based on MultiFusion–ChronoNet–AMCYan Yan0Yong Qian1Yan Zhou2State Grid Ningxia Electric Power Research Institute, Yinchuan 750011, ChinaState Grid Ningxia Electric Power Research Institute, Yinchuan 750011, ChinaSchool of Electronic Engineering, Jiangsu Ocean University, Lianyungang 222005, ChinaAccurate forecasting is crucial for enhancing the flexibility and controllability of power grids. Traditional forecasting methods mainly focus on modeling based on a single data source, which leads to an inability to fully capture the underlying relationships in wind power data. In addition, current models often lack dynamic adaptability to data characteristics, resulting in lower prediction accuracy and reliability under different time periods or weather conditions. To address the aforementioned issues, an ultra-short-term hybrid probabilistic prediction model based on MultiFusion, ChronoNet, and adaptive Monte Carlo (AMC) is proposed in this paper. By combining multi-source data fusion and a multiple-gated structure, the nonlinear characteristics and uncertainties of wind power under various input conditions are effectively captured by this model. Additionally, the AMC method is applied in this paper to provide comprehensive, accurate, and flexible ultra-short-term probabilistic predictions. Ultimately, experiments are conducted on multiple datasets, and the results show that the proposed model not only improves the accuracy of deterministic prediction but also enhances the reliability of probabilistic prediction intervals.https://www.mdpi.com/1996-1073/18/7/1646wind powerultra-short termMultiFusionChronoNetadaptive Monte Carloprobabilistic prediction |
| spellingShingle | Yan Yan Yong Qian Yan Zhou Nonparametric Probabilistic Prediction of Ultra-Short-Term Wind Power Based on MultiFusion–ChronoNet–AMC Energies wind power ultra-short term MultiFusion ChronoNet adaptive Monte Carlo probabilistic prediction |
| title | Nonparametric Probabilistic Prediction of Ultra-Short-Term Wind Power Based on MultiFusion–ChronoNet–AMC |
| title_full | Nonparametric Probabilistic Prediction of Ultra-Short-Term Wind Power Based on MultiFusion–ChronoNet–AMC |
| title_fullStr | Nonparametric Probabilistic Prediction of Ultra-Short-Term Wind Power Based on MultiFusion–ChronoNet–AMC |
| title_full_unstemmed | Nonparametric Probabilistic Prediction of Ultra-Short-Term Wind Power Based on MultiFusion–ChronoNet–AMC |
| title_short | Nonparametric Probabilistic Prediction of Ultra-Short-Term Wind Power Based on MultiFusion–ChronoNet–AMC |
| title_sort | nonparametric probabilistic prediction of ultra short term wind power based on multifusion chrononet amc |
| topic | wind power ultra-short term MultiFusion ChronoNet adaptive Monte Carlo probabilistic prediction |
| url | https://www.mdpi.com/1996-1073/18/7/1646 |
| work_keys_str_mv | AT yanyan nonparametricprobabilisticpredictionofultrashorttermwindpowerbasedonmultifusionchrononetamc AT yongqian nonparametricprobabilisticpredictionofultrashorttermwindpowerbasedonmultifusionchrononetamc AT yanzhou nonparametricprobabilisticpredictionofultrashorttermwindpowerbasedonmultifusionchrononetamc |