Wind Power Forecasting Using Dynamic Bayesian Models

This paper presents the development of a novel dynamic Bayesian network (DBN) model devoted to wind forecasting. An original procedure was developed to approximate this model, based on historical information in the form of time series. The DBN structure and parameters are learned from historical data, and this methodology can be applied to any prediction problem. In contrast to previous approaches, the proposed model considers all the relevant variables in the domain and produces a probability distribution for the predictions; providing important additional information to the decision makers. The method was evaluated experimentally with real data from a wind farm in Mexico for a time horizon of 5 hours, showing superior performance to traditional time-series prediction techniques.