Accurate medium-term wind power forecasting in a censored classification framework - Carsten Croonenbroeck; Christian Møller Dahl (European University Viadrina, Department of Business Administration and Economics, 2014)
We provide a wind power forecasting methodology that exploits many of the actual data's statistical features, in particular both-sided censoring. While other tools ignore many of the important stylized facts or provide forecasts for short-term horizons only, our approach focuses on medium-term forecasts, which are especially necessary for practitioners in the forward electricity markets of many power trading places; for example, NASDAQ OMX Commodities (formerly Nord Pool OMX Commodities) in northern Europe. We show that our model produces turbine-specific forecasts that are significantly more accurate in comparison to established benchmark models and present an application that illustrates the financial impact of more accurate forecasts obtained using our methodology.
Keywords: C34; E27; Q47; 330; Censored Regression; Wind Energy; Forecasting