Ensemble forecasting system for short-term wind speed forecasting based on optimal sub-model selection and multi-objective version of mayfly optimization algorithm
•An ensemble forecasting system is developed for short-term wind speed.•A comprehensive indicator is proposed to determine the best sub-models adaptively.•Point and interval prediction are conducted for more intelligent grid management.•Optimal distribution is used effectively to measure the uncerta...
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| Published in: | Expert systems with applications Vol. 177; p. 114974 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York
Elsevier Ltd
01.09.2021
Elsevier BV |
| Subjects: | |
| ISSN: | 0957-4174, 1873-6793 |
| Online Access: | Get full text |
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| Abstract | •An ensemble forecasting system is developed for short-term wind speed.•A comprehensive indicator is proposed to determine the best sub-models adaptively.•Point and interval prediction are conducted for more intelligent grid management.•Optimal distribution is used effectively to measure the uncertainty.•Multi-objective version of Mayfly Algorithm is proposed for ensemble forecasting.
Wind energy has attracted considerable attention in the past decades as a low-carbon, environmentally friendly, and efficient renewable energy. However, the irregularity of wind speed makes it difficult to integrate wind energy into smart grids. Thus, achieving credible and effective wind speed forecasting results is crucial for the operation and management of wind energy. In this study, we propose an ensemble forecasting system that integrates data decomposition technology, sub-model selection, a novel multi-objective version of the Mayfly algorithm, and different predictors to better demonstrate the stochasticity and fluctuation of wind speed data. After decomposition using the data decomposition technology, each decomposed wind speed series is considered as the input to multiple predictors, from which the optimal forecasting model for each sub-series is determined based on sub-model selection. To obtain reliable forecasting results, a novel multi-objective version of the Mayfly algorithm is proposed to estimate the optimal weight coefficients for integrating the forecasting values of the sub-series. Based on three experiments and four analyses, the proposed ensemble system is verified as effective for obtaining accurate and stable point forecasting and interval forecasting performances, thus aiding in the planning and dispatching of power grids. |
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| AbstractList | Wind energy has attracted considerable attention in the past decades as a low-carbon, environmentally friendly, and efficient renewable energy. However, the irregularity of wind speed makes it difficult to integrate wind energy into smart grids. Thus, achieving credible and effective wind speed forecasting results is crucial for the operation and management of wind energy. In this study, we propose an ensemble forecasting system that integrates data decomposition technology, sub-model selection, a novel multi-objective version of the Mayfly algorithm, and different predictors to better demonstrate the stochasticity and fluctuation of wind speed data. After decomposition using the data decomposition technology, each decomposed wind speed series is considered as the input to multiple predictors, from which the optimal forecasting model for each sub-series is determined based on sub-model selection. To obtain reliable forecasting results, a novel multi-objective version of the Mayfly algorithm is proposed to estimate the optimal weight coefficients for integrating the forecasting values of the sub-series. Based on three experiments and four analyses, the proposed ensemble system is verified as effective for obtaining accurate and stable point forecasting and interval forecasting performances, thus aiding in the planning and dispatching of power grids. •An ensemble forecasting system is developed for short-term wind speed.•A comprehensive indicator is proposed to determine the best sub-models adaptively.•Point and interval prediction are conducted for more intelligent grid management.•Optimal distribution is used effectively to measure the uncertainty.•Multi-objective version of Mayfly Algorithm is proposed for ensemble forecasting. Wind energy has attracted considerable attention in the past decades as a low-carbon, environmentally friendly, and efficient renewable energy. However, the irregularity of wind speed makes it difficult to integrate wind energy into smart grids. Thus, achieving credible and effective wind speed forecasting results is crucial for the operation and management of wind energy. In this study, we propose an ensemble forecasting system that integrates data decomposition technology, sub-model selection, a novel multi-objective version of the Mayfly algorithm, and different predictors to better demonstrate the stochasticity and fluctuation of wind speed data. After decomposition using the data decomposition technology, each decomposed wind speed series is considered as the input to multiple predictors, from which the optimal forecasting model for each sub-series is determined based on sub-model selection. To obtain reliable forecasting results, a novel multi-objective version of the Mayfly algorithm is proposed to estimate the optimal weight coefficients for integrating the forecasting values of the sub-series. Based on three experiments and four analyses, the proposed ensemble system is verified as effective for obtaining accurate and stable point forecasting and interval forecasting performances, thus aiding in the planning and dispatching of power grids. |
| ArticleNumber | 114974 |
| Author | Wang, Jianzhou Jiang, Ping Zhang, Lifang Liu, Zhenkun |
| Author_xml | – sequence: 1 givenname: Zhenkun surname: Liu fullname: Liu, Zhenkun email: zhenkunliudufe@163.com organization: School of Statistics, Dongbei University of Finance and Economics, No. 217, Jianshan Road, Shahekou District, Dalian, Liaoning Province 116025, China – sequence: 2 givenname: Ping surname: Jiang fullname: Jiang, Ping email: pjiang@dufe.edu.cn organization: School of Statistics, Dongbei University of Finance and Economics, No. 217, Jianshan Road, Shahekou District, Dalian, Liaoning Province 116025, China – sequence: 3 givenname: Jianzhou surname: Wang fullname: Wang, Jianzhou email: wangjz@dufe.edu.cn organization: School of Statistics, Dongbei University of Finance and Economics, No. 217, Jianshan Road, Shahekou District, Dalian, Liaoning Province 116025, China – sequence: 4 givenname: Lifang surname: Zhang fullname: Zhang, Lifang organization: School of Statistics, Dongbei University of Finance and Economics, No. 217, Jianshan Road, Shahekou District, Dalian, Liaoning Province 116025, China |
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| Keywords | Multi-objective version of Mayfly algorithm Sub-model selection Short-term wind speed forecasting Ensemble model |
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| Snippet | •An ensemble forecasting system is developed for short-term wind speed.•A comprehensive indicator is proposed to determine the best sub-models... Wind energy has attracted considerable attention in the past decades as a low-carbon, environmentally friendly, and efficient renewable energy. However, the... |
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| SubjectTerms | Algorithms Decomposition Electric power grids Ensemble model Forecasting Mathematical models Multi-objective version of Mayfly algorithm Multiple objective analysis Optimization Short-term wind speed forecasting Smart grid Sub-model selection Wind power Wind speed |
| Title | Ensemble forecasting system for short-term wind speed forecasting based on optimal sub-model selection and multi-objective version of mayfly optimization algorithm |
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