Accuracy of near real time updates in wind power forecasting with regard to different weather regimes
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1 Accuracy of near real time updates in wind power forecasting with regard to different weather regimes Nadja Saleck 1, Florian Bertsch 2, Lüder von Bremen 1, Detlev Heinemann 1 1 ForWind, University of Oldenburg, Germany 2 Department of Computer Science, Humboldt-Universität zu Berlin, Germany 7th EMS Annual Meeting 8th European Conference on Applications of Meteorology San Lorenzo de El Escorial, Spain, October 2007
2 Outline Study site Wind power forecasting - method Cluster analysis method and results Observed power by clusters Forecast errors by clusters Conclusions
3 Study site North-West-Germany single wind farms ( ) lat ( ) lon
4 Wind power forecast data ( 2006 (2004 input observed wind power objective forecast wind power of the next 4 hours without wind speed information from weather forecasts (Numerical Weather Prediction) method Neural Networks observation basetime forecast ( future ) unknown unknown training data
5 Clustering data: 500 hpa heights from ECMWF analysis data (6-hourly), Jan April 2007 Principal Component ( PCA ) Analysis reduction of data take as much components to have 99 % of explained variance Cluster analysis k-mean clustering separately for: summer (April - Sept) and winter (Oct. - March) relate single clusters to points in time time Zeit cluster Cluster 03-May :00: May :00: May :00: May :00: May :00: May :00: May :00: May :00: May :00: May :00: May :00: May :00: May :00: May :00: May :00: May :00:00 5
6 Summer - Cluster 500 hpa level January April 2007 Cluster No. 1 Cluster No. 2 Cluster No. 3 Cluster No.4 Cluster No. 5 Cluster No. 6 Cluster No. 7 gpdm
7 sea level January April 2007 Summer - Cluster Cluster No. 1 Cluster No. 2 Cluster No. 3 Cluster No.4 Cluster No. 5 Cluster No. 6 Cluster No. 7 hpa
8 Winter - Cluster 500 hpa level January April 2007 Cluster No. 1 Cluster No. 2 Cluster No. 3 Cluster No.4 Cluster No. 5 Cluster No. 6 Cluster No. 7 gpdm
9 sea level January April 2007 Winter - Cluster Cluster No. 1 Cluster No. 2 Cluster No. 3 Cluster No.4 Cluster No. 5 Cluster No. 6 Cluster No. 7 hpa
10 Observed wind power input for different clusters Summer Winter
11 Forecast errors (RMSE) of wind power forecasts depending on clusters - winter NN (training ( farms with three wind ( power RMSE (% inst. ( ) lat persistence NN (training ( farm with one wind lon ( ) ( h ) look ahead time ( ) lat forecast: one wind farm ( ), 2005 training with data of Winter cluster sea level pressure lon ( )
12 Traing within clusters Forecast errors (RMSE) - winter ( power RMSE (% inst. ( ) lat lon ( ) persistence ( data NN (training with complete NN (training within the single clusters) ( h ) look ahead time forecast: one wind farm ( ), 2006 training with data of 2005, separatly for each cluster 5 6 7
13 Conclusions near real time updates, require: near real time wind power data advantage: no NWP data necessary very actual shortest term forecasts possible ( clusters ) wind power input and forecast errors depend on weather situation for some clusters improvements are possible (as shown): consideration of geographical distribution of the wind farms training differentiation by clusters Perspectives larger data set including more wind farms more sophisticated methods to capture spatial patterns apply different methods: Neural Networks, autoregressive models combine with model using NWP
14 Thank you for your attention. this work was funded by EWE AG ForWind Center for wind energy research Carl von Ossietzky University Oldenburg, Germany
15 Forecast errors (RMSE) of wind power forecasts depending on clusters - summer NN (training ( farms with three wind ( power RMSE (% inst. ( ) lat persistence NN (training ( farm with one wind lon ( ) ( h ) look ahead time ( ) lat forecast: one wind farm ( ), 2005 training with data of Summer cluster sea level pressure lon ( )
16 Summer Cluster temporal distribution (%) occurence of each cluster Cluster No. 1 Cluster No. 2 Cluster No. 3 Cluster No. 5 Cluster No. 6 Cluster No. 7 Cluster No.4 (%) occurence number of cluster
17 Winter Cluster temporal distribution (%) occurence of each cluster Cluster No. 1 Cluster No. 2 Cluster No. 3 Cluster No. 5 Cluster No. 6 Cluster No. 7 Cluster No.4 (%) occurence number of cluster
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