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An Application of Ensemble/Multi Model Approach for Wind Power Production Forecasting : Volume 6, Issue 1 (28/02/2011)

By Alessandrini, S.

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Book Id: WPLBN0003989867
Format Type: PDF Article :
File Size: Pages 3
Reproduction Date: 2015

Title: An Application of Ensemble/Multi Model Approach for Wind Power Production Forecasting : Volume 6, Issue 1 (28/02/2011)  
Author: Alessandrini, S.
Volume: Vol. 6, Issue 1
Language: English
Subject: Science, Advances, Science
Collections: Periodicals: Journal and Magazine Collection (Contemporary), Copernicus GmbH
Historic
Publication Date:
2011
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Hagedorn, R., Alessandrini, S., Pinson, P., Sperati, S., & Decimi, G. (2011). An Application of Ensemble/Multi Model Approach for Wind Power Production Forecasting : Volume 6, Issue 1 (28/02/2011). Retrieved from http://worldlibrary.net/


Description
Description: RSE, Ricerca Sistema Energetico, Milano, Italy. The wind power forecasts of the 3 days ahead period are becoming always more useful and important in reducing the problem of grid integration and energy price trading due to the increasing wind power penetration. Therefore it's clear that the accuracy of this forecast is one of the most important requirements for a successful application. The wind power forecast applied in this study is based on meteorological models that provide the 3 days ahead wind data. A Model Output Statistic correction is then performed to reduce systematic error caused, for instance, by a wrong representation of surface roughness or topography in the meteorological models. For this purpose a training of a Neural Network (NN) to link directly the forecasted meteorological data and the power data has been performed. One wind farm has been examined located in a mountain area in the south of Italy (Sicily). First we compare the performances of a prediction based on meteorological data coming from a single model with those obtained by the combination of models (RAMS, ECMWF deterministic, LAMI). It is shown that the multi models approach reduces the day-ahead normalized RMSE forecast error (normalized by nominal power) of at least 1% compared to the singles models approach. Finally we have focused on the possibility of using the ensemble model system (EPS by ECMWF) to estimate the hourly, three days ahead, power forecast accuracy. Contingency diagram between RMSE of the deterministic power forecast and the ensemble members spread of wind forecast have been produced. From this first analysis it seems that ensemble spread could be used as an indicator of the forecast's accuracy at least for the first three days ahead period.

Summary
An application of ensemble/multi model approach for wind power production forecasting

Excerpt
Von Bremen, L.: Combination of deterministic and probabilistic meteorological models to enhance wind farm forecast, J. Phys. Conf. Ser., 75, 1–8, 2007.; Pielke, R. A., Cotton, W. R., Walko, R. L., Tremback, C. J., Lyons, W. A., Grasso, L. D., Nicholls, M. E., Moran, M. D., Wesley, D. A., Lee, T. J., and Copeland, J. H.: A Comprehensive Meteorological Modeling System – RAMS, Meteorol. Atmos. Phys., 49, 69–91, 1992.; Pinson, P.: Adaptive recalibration of ensemble forecasts of (u,v)-winds, Technical report, ECMWF, 2011.

 

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