A Bayesian model for wind farm capacity factors

Capacity factors are an important performance metric for offshore wind energy projects as they indicate how efficiently a given project generates electricity. Given the intermittent nature of resource, there is substantial variability between observed capacity seasonally and years. However, little work has focused on extracting trends in variable farm generation data. This paper proposes applying hierarchical Bayesian techniques to historical enable prediction factor distributions. The proposed model relies data from UK farms, most developed market energy, but equally applicable other countries. resulting distributions highlight both when modelled yearly monthly (which accounts seasonality). shows that newer farms have higher than older with improvement approximately 20%. It also demonstrates smaller impact levelized cost estimates. results this study can be used predict individual or make predictions generic farm.