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Dynamic Forecast Combinations of Improved Individual Forecasts for the Prediction of Wind Energy

  • We study the prediction performance of different improved individual wind energy forecasts in various static and dynamic ombination processes. To this end, we develop a combined error minimization model (CEMM) based on nonlinear functions. This approach reflects the nonlinear nature of weather and especially of wind energy prediction problems. Based on the model, we construct significantly improved individual forecasts. The corresponding time dependent model coefficients are determined by dynamic OLS (ordinary least squares) regression and Kalman filter methods. The former method shows a slightly better performance than the Kalman filter based approaches. Further improvements can be achieved by a combination of these improved wind energy forecasts. In this case, the combination coefficients are calculated from a static and two dynamic OLS regressions. The resulting forecasts are characterized by a further increased prediction accuracy compared to the combination of the uncorrected forecast data and can outperform a given benchmark.

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Metadaten
Author: Mathias Käso, Felix MüsgensORCiD, Oliver Grothe
URL:http://ieeexplore.ieee.org/document/7521228/
DOI:https://doi.org/10.1109/EEM.2016.7521228
ISBN:978-1-5090-1298-5
Title of the source (English):IEEE Conference Proceedings EEM 2016
Publisher:IEEE
Place of publication:Piscataway, NJ
Document Type:Conference Proceeding
Language:English
Year of publication:2016
Number of pages:5
Faculty/Chair:Fakultät 3 Maschinenbau, Elektro- und Energiesysteme / FG Energiewirtschaft
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