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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Vydáno v:Expert systems with applications Ročník 177; s. 114974
Hlavní autoři: Liu, Zhenkun, Jiang, Ping, Wang, Jianzhou, Zhang, Lifang
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.09.2021
Elsevier BV
Témata:
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
Multi-objective version of Mayfly algorithm
Sub-model selection
Short-term wind speed forecasting
Ensemble model
ISSN:0957-4174, 1873-6793
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Shrnutí:•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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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.114974