Wind power forecasting for the villonaco wind farm using AI techniques

Abstract

Forecasting represents a very important task for planning, control and decision making in many fields. Forecasting the dollar price is important for global companies to plan their investments. Forecasting the weather is determinant to make the decision of either giving a party outdoors or indoors. Forecasting the behaviour of a process represents the key factor in predictive control. In this paper, we present a methodology to build wind power forecasting models from data using a combination of artificial intelligence techniques such as artificial neural networks and dynamic Bayesian nets. These techniques allow obtaining forecast models with different characteristics. Finally, a model recalibration function is applied to raw discrete models in order to gain an extra accuracy. The experiments ran for the unit 1 of the Villonaco wind farm in Ecuador demonstrated that the selection of the best predictor can be more useful than selecting a single high-efficiency approach.