Joint modelling wind speed and power via Bayesian Dynamical models

The relationship of dependence between wind speed and wind power variables has a degree of complexity that has motivated several scientific studies over the years. Much of this research seeks to understand the stochastic nature of both phenomena, either for the purpose of marginal analysis or for joint analyses, aiming to improve prediction of wind power. The present study proposes three dynamic Bayesian models for wind energy that account for the complexity of both variables via hierarchical structures such as temporal dependence, nonstationary behaviour, and truncation of power due to turbine specifications. This hierarchy considers wind energy modelling conditioned on wind speed and a marginal model for wind speed. This approach allows joint analysis to be carried out via univariate time series modelling. Analysis of a rich dataset from Bahia state (Brazil) indicates that the proposed models are accurate for both short-term and long-term wind power forecasts. (c) 2022 Elsevier Ltd. All rights reserved.