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The Analog Ensemble for Renewable Energy Applications: an Overview

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The Analog Ensemble for Renewable Energy Applications: an Overview The Analog Ensemble for Renewable Energy Applications: an Overview Luca Delle Monache1 Stefano Alessandrini1, Guido Cervone2, Constantin Junk3, Daran Rife4, Jessica Ma4, Simone Sperati5, Sue Ellen Haupt1, Thomas Brummet1, Paul Prestopnik1, Gerry Wiener1, Jesper Nissen6, Sam Hawkins6 1National Center for Atmospheric Research − Boulder, CO, USA 2The Pennsylvania State University − State College, PA, USA 3ForWind, Car von Ossietzky University Oldenburg − Oldenburg, Germany 4DNV GL − USA 5RSE − Milan, Italy 6Vattenfall − Sweden rd 3 International Conference Energy & Meteorology − Boulder, CO, USA, 25 June 2015 1 Acknowledgments • COLLABORATORS o Roland Stull (University of British Columbia) o Lueder von Bremen, Detlev Heinemann (ForWind, Carl von Ossietzky University) o Iris Odak, Kristian Horvath (Meteorological and Hydrological Service of Croatia) o Badrinath Nagarajan (IBM) o Federica Davo’ (RSE) o Tony Eckel (Microsoft) o Thomas Nipen (Norwegian Meteorological Institute) o Laura Harding (The Pennsylvania State University) o Irina Djalalova, Jim Wilczak (NOAA) o Emilie Vanvyve (UK Met Office) o Jens Madsen (Vattenfall) o Christopher Rozoff, Will Lewis (University of Wisconsin, Madison) o Jan Keller (DWD, University of Bonn) • SPONSORS o U.S. Department of Defense (DOD) Army Test and Evaluation Command (ATEC) o U.S. DOD Defense Threat Reduction Agency (DTRA) o U.S. Department of Energy (DOE) o U.S. National Aeronautics and Space Administration (NASA) o U.S. National Renewable Energy Laboratory (NREL) o U.S. National Oceanic and Atmospheric Administration (NOAA) Hurricane Forecast Improvement Program (HFIP) o Vattenfall o Vestas Wind Systems o Xcel Energy 2 Outline • Analog Ensemble (AnEn) basic idea • AnEn successful applications AnEn for short-term (i.e., 0-72 h) wind power predictions AnEn for short-term (i.e., 0-72 h) solar GHI predictions AnEn for long-term (i.e., multi-year) wind resource assessment • Summary of recurring features of AnEn 3 AnEn has been successfully applied for: • Short-term predictions of: 10- and 80-m wind speed, 2-m temperature, etc. Wind power Solar GHI Load • Downscaling, resource assessment: Wind speed Computationally efficient dynamical downscaling 4 AnEn has been successfully applied for: • Short-term predictions of: 10- and 80-m wind speed, 2-m temperature, etc. Horvath (DHMZ) et al., earlier today Wind power Lueder von Bremen (ForWind) et al., earlier today Solar GHI Load Alessandrini (NCAR) et al., next talk • Downscaling, resource assessment: Wind speed Nissen (Vattenfall) et al., Tuesday Computationally efficient dynamical downscaling Daran Rife (DNV GL) et al., Tuesday 5 AnEn has been successfully applied for: • Short-term predictions of: 10- and 80-m wind speed, 2-m temperature, etc. Wind power Solar GHI Load • Downscaling, resource assessment: Wind speed Computationally efficient dynamical downscaling 6 Analog Ensemble (AnEn) Mon Tue Wed Thu Fri Sat Sun t = 0 Time 7 Analog Ensemble (AnEn) PRED Mon Tue Wed Thu Fri Sat Sun t = 0 Time 8 Analog Ensemble (AnEn) PRED OBS Mon Tue Wed Thu Fri Sat Sun t = 0 Time 9 Analog Ensemble (AnEn) PRED OBS Mon Tue Wed Thu Fri Sat Sun t = 0 Time 10 Analog Ensemble (AnEn) PRED OBS Mon Tue Wed Thu Fri Sat Sun t = 0 Time 11 Analog Ensemble (AnEn) PRED Mon Tue Wed Thu Fri Sat Sun t = 0 Time 12 Analog Ensemble (AnEn) PRED OBS Mon Tue Wed Thu Fri Sat Sun t = 0 Time Wed Fri Sat Tue Sun Mon Thu farthest closest analog analog “Analog” Space Delle Monache et al. (MWR 2011, 2013) 13 Analog Ensemble (AnEn) PRED OBS Mon Tue Wed Thu Fri Sat Sun t = 0 Time Wed Fri Sat Tue Sun Mon Thu farthest closest analog analog “Analog” Space 14 Analog Ensemble (AnEn) PRED OBS Mon Tue Wed Thu Fri Sat Sun t = 0 Time 2-member AnEn Wed Fri Sat Tue Sun Mon Thu farthest closest analog analog “Analog” Space 15 Power predictions: Experiment design • Test site: Wind farm in northern Sicily − 9 turbines, 850 kW Nominal Power (NP) • Training period: November 2010 - October 2012 • Verification period: November 2011 – October 2012 • Probabilistic prediction systems: ECMWF EPS, COSMO LEPS, AnEn Alessandrini et al. (RE 2015) 16 RMSE of ensemble means Analog Ensemble (AnEn) Mean European Center for Medium range Weather Forecasting (ECMWF) Ensemble Mean 17 SMUD observations data set • 8 stations with available global horizontal irradiance (GHI) observations • GHI data for the 226-day period, from 04-25-2014 to 12-03-2014) • Average missing data: ~ 9% • So far only 3 stations are used (those with less QC issues) Alessandrini et al. (AE 2015) 18 Results: Time series example: AnEn, DICAST Station SMUD 67, forecast initialized at 12 UTC, 20 September 2014 0 0 9 0 0 7 5- 95% ) 2 25-75% − An-En Mean m 0 0 W ( 5 I H G 0 0 3 0 0 1 0 0 3 6 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 Lead Time (Hour) 19 AnEn vs. DICAST (Brier Skill Score) • The Brier Skill Score (BSS) compares the skill of two probabilistic predictions for a given event • Values > 0 indicate that AnEn has more skill than DICAST • Shown is the BSS index as a function of forecast lead time, with the event considered being GHI > mean(obs. GHI at the given lead time) 0 8 0 6 0 4 Conclusion: AnEn is nearly 0 ) 2 AnEn better AnEn always better than the already % ( 0 S optimized DICast forecast, even 1 S − B with this short training and 0 4 validation period − 0 7 − 0 AnEn worse AnEn 0 1 − 1 5 9 14 20 26 32 38 44 50 56 62 68 Lead Time (Hours) 20 AnEn for wind resource assessment • Recreate a long-term observation-based wind climatology at site • Downscale a long-term NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) time series using a short-term record of observations Observed wind speed MERRA wind speed 1992 19 years to downscale 2010 1 year where 2011 MERRA/obs. overlap Vanvyve et al. (RE 2015), Zhang et al. (AE 2015) 21 AnEn for wind resource assessment • Recreate a long-term observation-based wind climatology at site • Downscale a long-term NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) time series using a short-term record of observations Observed wind speed MERRA wind speed 1992 19 years to downscale 2010 1 year where 2011 MERRA/obs. overlap 22 AnEn for wind resource assessment • Recreate a long-term observation-based wind climatology at site • Downscale a long-term NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) time series using a short-term record of observations Observed wind speed MERRA wind speed 1992 19 years to downscale 2010 1 year where 2011 MERRA/obs. overlap 23 AnEn for wind resource assessment • Recreate a long-term observation-based wind climatology at site • Downscale a long-term NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) time series using a short-term record of observations Observed wind speed MERRA wind speed 1992 19 years to downscale 2010 1 year where 2011 MERRA/obs. overlap 24 AnEn for wind resource assessment • Recreate a long-term observation-based wind climatology at site • Downscale a long-term NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) time series using a short-term record of observations Observed wind speed MERRA wind speed 1992 19 years to downscale 2010 1 year where 2011 MERRA/obs. overlap 25 AnEn for wind resource assessment • Recreate a long-term observation-based wind climatology at site • Downscale a long-term NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) time series using a short-term record of observations Analog ensemble Observed (deterministic + probabilistic) wind speed MERRA wind speed 1992 19 years to downscale 2010 1 year where 2011 MERRA/obs. overlap 26 Downscaled wind tower locations 100m & 50m 27 Hourly wind speed Hourly wind speed Wind speed at Boulder, CO: Wind speed at Butler: WindHistogram distributions comparisonHistogram 0.20 Boulder, CO Observations Butler, WA Observations Training: 1 year MERRA Training: 1 year MERRA Downscaling: 3 years Downscaling: 5 years Analog ensemble 0.12 Analog ensemble 0.16 y 0.09 y t Hourly wind speed t i i s Outlier s Outlier 0.12 n Wind speed at Butler: n e th e th 75 75 d d Histogram Inside Inside y Mean y Mean t t i inner i th inner th l l i i 50 50 b fence b fence th th a 25 a 25 b b 0.06 o o r Outlier r 0.08 Outlier P P Observations MERRA Hourly wind speed Hourl0y .w0in3d speed 0.04 Wind speed at Goodnoe Hills, WA: 0.12 Wind speed at Utah: Analog ensemble Histogram Histogram 0.00 0.00 y 0.09 0 1 2 3 4 5 6 7 8 9 10 11121314151617181920 2122232425 30 0 1 2 3 4 5 6 t 7 8 9 10 11121314151617181920 2122232425 30 i -1 s -1 Outlier 0.16 Wind speed (m s ) n Wind speed (m s ) e 75th d Observations Observations Inside y Mean t i inner th l MERRA Training period: last 365 days, period downscaled: last 3 entire yeMaErsR, RanAalogs: 25 0.16 Training period: i last 365 days, period downscaled: last 5 entire years, analogs: 25 50 b fence th Goodnoe Hills, WAAna log ensemble a Utah Prison, UT Analog ensemble 25 b 0.06 Training: 1 year o Training: 1 year r Outlier Training: 2 Jan 2012 to 31 Dec 2012, period: 2 Jan 2009 to 1 Jan 2012, trend: 4 neDownscaling:ighbors, window: 0 nei g3hb yearsor, var: w s.wd Training: 4 Aug 2007 to 2 Aug 2008, peP riod: 5 Aug 2002 to 3 Aug 2007, trend: Downscaling:4 neighbors, window: 0 1 n eyearsighbor, v ar: ws.wd 0.12 0.12 y y t t i 0.03 i s Outlier s Outlier n n e th e th 75 75 d d Inside Inside y Mean y Mean t t i inner th i inner th l l 0.08 i 50 i 50 b fence b fence th th a 25 a 25 0.08 b b o 0.00 o r Outlier r Outlier P P 0 1 2 3 4 5
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