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Title Optimal combined wind power forecasts using exogeneous variables
Author Thordarson, Fannar Ørn (Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
Supervisor Madsen, Henrik (Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
Nielsen, Henrik Aalborg (Mathematical Statistics, Informatics and Mathematical Modelling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
Institution Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark
Thesis level Master's thesis
Year 2007
Abstract The aim of combining forecasts is to reduce variation from observed values by compositing two or more forecasts, which predict for the same event at the same time. Many methods have developed since the problem was presented, varying from a method of equal weights to more complex methods e.g. state space. Despite the complexity a linear model of the combination appears to be the most favorable where the parameters of the forecasts are summing to one. The parameters, also called weights, are unknown and need to be estimated to get optimal combined forecast. In this report the problem of combining forecasts is addressed by (i) estimate weights by local regression and compare with RLS and minimum variance methods, which are well known procedures when combining, and (ii) using information from meteorological forecasts to estimate the forecast weights with local regression. The methods are applied to the Klim wind farm using three WPPT forecasts based on different weather forecasting systems. It is shown how the prediction is improved when the forecasts are combined by using locally fitted linear model and when it outperforms the RLS estimation which is also considered. Furthermore, the meteorological forecasts from DMI-HIRLAM are inspected and the air density (ad) and the turbulent kinetic energy at pressure level 38 (tke) are used as regressors for locally fitting the weights into the linear model. The results in this report show that using the meteorological information to estimate the weights does not outperform the RLS method but does give reasonable fit, which can be elevated by further analysis.
Series IMM-Thesis
Fulltext
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Admin Creation date: 2007-06-11    Update date: 2008-10-29    Source: dtu    ID: 200734    Original MXD