Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system

Wind energy is an increasing concern for wind farm administrators. Effective wind energy potential analysis and accurate forecasting can reduce the operating cost of wind farms. However, many previous studies have been restricted to analyses of wind energy potential analysis and wind speed forecasti...

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Veröffentlicht in:Renewable energy Jg. 134; S. 681 - 697
Hauptverfasser: Zhao, Xuejing, Wang, Chen, Su, Jinxia, Wang, Jianzhou
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.04.2019
Schlagworte:
algorithms
artificial intelligence
Data preprocessing
decision making
farm management
operating costs
Swarm intelligence optimization algorithm
swarms
time series analysis
Weibull distribution
Weibull statistics
Wind energy potential analysis
wind farms
wind power
wind speed
Wind energy potential analysis
Swarm intelligence optimization algorithm
Weibull distribution
Data preprocessing
ISSN:0960-1481, 1879-0682
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Zusammenfassung:Wind energy is an increasing concern for wind farm administrators. Effective wind energy potential analysis and accurate forecasting can reduce the operating cost of wind farms. However, many previous studies have been restricted to analyses of wind energy potential analysis and wind speed forecasting, which may result in poor decisions and inaccurate power scheduling for wind farms. This study develops a wind energy decision system based on swarm intelligence optimization and data preprocessing, which includes two modules: wind energy potential analysis and wind speed forecasting. In the wind energy potential analysis module, the parameters of the Weibull distribution are optimized by a multiple swarm intelligence optimization algorithm, which can provide better wind energy assessment results. In the wind speed forecasting module, the data preprocessing method can effectively eliminate the noise of the original wind speed time series, maintain the characteristics of the wind speed data, and improve the accuracy of the forecasting model. The numerical results show that the wind energy decision system not only provides an effective wind energy assessment, but can also satisfactorily approximate the actual wind speed forecasting. Therefore, it can serve as an effective tool for wind farm management and decision-making. •The developed wind farm decision system includes two modules: Wind energy assessment and wind speed forecasting modules.•Using multiple swarm intelligence optimization to optimize the parameter of Weibull and get an optimum PDF of Weibull.•Provide an effective method for choosing the de-noising model.•A modified optimization is proposed that optimizes the weight and bias matrices of nonlinear ELMs.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:0960-1481
1879-0682
DOI:10.1016/j.renene.2018.11.061