Using the Cloud-Bayesian Network in Environmental Assessment of Offshore Wind-Farm Siting

Offshore wind energy has become the fastest growing form of renewable energy for the last few years. And the development of offshore wind farms (OWFs) is now characterized by a boom. OWF siting is crucial in the success of wind energy projects. Therefore, this paper aims to introduce intelligent alg...

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Bibliographic Details
Published in:Mathematical problems in engineering 2019, Vol.2019 (2019), p.1-15
Main Authors: Hong, Mei, Zhang, Ren, Liu, Kefeng, Li, Ming, Pan, Qin
Format: Article
Language:English
Subjects:
Adaptive algorithms
Algorithms
Alternative energy sources
Artificial intelligence
Bayesian analysis
Computer engineering
Decision making
Environmental assessment
Environmental impact
Fuzzy logic
Geographic information systems
Indicators
Offshore
Offshore energy sources
Optimization
Principal components analysis
Studies
Threat assessment
Wind farms
Wind power
Wind power generation
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Summary:Offshore wind energy has become the fastest growing form of renewable energy for the last few years. And the development of offshore wind farms (OWFs) is now characterized by a boom. OWF siting is crucial in the success of wind energy projects. Therefore, this paper aims to introduce intelligent algorithms to improve the siting assessment under conditions of multisource and uncertain information. An optimization macrositing model based on Cloud-Bayesian Network (Cloud-BN) is put forward. We introduce the cloud model and adaptive Gaussian cloud transformation (A-GCT) algorithm to grade indicators and apply BN to achieve nonlinear integration and inference of multi-indicators. Combined with the fuzzy representation of the cloud model and probabilistic reasoning of BN, the proposed model can investigate the most efficient siting areas of OWFs in the North Sea of Europe. The experimental results indicate that the siting accuracy is up to 86.67% with reference to the actual OWF location.
ISSN:1024-123X
1563-5147
DOI:10.1155/2019/9710839