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

Abstract

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.

Standardizing the factors used in wind farm site suitability models: A review

“…GIS-based modeling approaches to Wind Farm Site Suitability (WiFSS) analysis have been performed in countries including Ecuador [ 78 ], India [ 79 ], Nigeria [ 80 ], Serbia [ 81 ], South Korea [ 82 ], Spain [ 83 ], the United States [ 84 ], to name a few. Alternative approaches to modeling WiFSS include Bayesian networks [ 85 , 86 ], logistic regression [ [87] , [88] , [89] ], and machine learning [ 90 , 91 ]. Although techniques for modeling WiFSS differ (e.g., Bayesian approaches quantify uncertainty in decision-making effectively but often lack the spatial explicitness of GIS approaches [ 92 ]), these techniques serve the common objectives of improving system understanding and informing the decision-making process for siting wind farms.…”