The Use of Analog Ensembles to Improve Short-Term Solar Irradiance Forecasting
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1 ALBANY BARCELONA BANGALORE AMS Annual Meeting Atlanta, GA February 6, 214 The Use of Analog Ensembles to Improve Short-Term Solar Irradiance Forecasting Steve Young and John W. Zack AWS Truepower, LLC 463 NEW KARNER ROAD ALBANY, NY 1225 awstruepower.com 211 AWS Truepower, LLC
2 Background AWS Truepower developed the Solar and Wind Integrated Forecast Tool (SWIFT) (Zack et. al., 1:3 PM). As part of SWIFT, a Pyramidal Image Matcher (PIM) cloud advec\on algorithm has been employed as a short term solar irradiance forecast tool. Analog Ensemble (AE) has been successfully applied to day- ahead NWP output by by Delle Monache, et. al. (213). Ques/on Can AE improve upon the PIM irradiance forecast? Delle Monache, Luca, F. Anthony Eckel, Daran L. Rife, Badrinath Nagarajan, Keith Searight, 213: Probabilistic Weather Prediction with an Analog Ensemble. Mon. Wea. Rev., 141,
3 Analog Ensemble Method 1) Compute one or more normalized case- matching variables for the current case and each case in the training sample 2) Case- matching score: distance between current case and a training sample case in case- matching variable space. Case Matching Parameter Current forecast case N closest matches Case Matching Parameter Forecast Value ) Choose the N cases with the lowest case matching scores Time 1 12 Observed Outcome Ensemble Mean
4 Analog Ensemble Details All case- matching variables are normalized. Ensemble is calculated independently at each forecast interval with some blending to prevent abrupt transi\ons. Case matching variables are chosen by trial and error using local forecas\ng knowledge.
5 Analog Ensemble Forecast Process Visible Satellite Image Irradiance software Ensemble of observed Analogs to current Forecast Situation at Each forecast Site Analog Ensemble Case Matching Score Components for each Forecast site Case Matching Score Definitions Gridded Observed Clear Sky Factor Image Matcher Regime_finder Gridded Cloud Displacement Vectors and Predicted Clear Sky Factor Forecast site locations
6 Pyramidal Image Matcher A>ributes Mul\- scale approach enables the PIM to capture the mo\on and development/dissipa\on of clouds at all important scales of mo\on. Es\mates coarse cloud mo\on vector field a larger scales using visible satellite images averaged to coarse resolu\on. Refines cloud mo\on vector field at successively finer scales un\l the full resolu\on image is reached. Es\mates future images by propaga\ng current image forward in \me using the mo\on vector field. ZINNER, T., H. MANNSTEIN, A. TAFFERNER, 28: Cb- TRAM: Tracking and monitoring severe convection from onset over rapid development to mature phase using multi- channel Meteosat-8 SEVIRI data. - Meteor. Atmos. Phys. 11, , DOI 1.17/s y.
7 Pyramidal Image Matcher Method Full 1 km Resolu\on Image 8 km Averaged Image 133 HST 14 HST
8 Pyramidal Image Matcher Configura/on Motion vector field is derived from the most recent 2 observed images at 16 km resolution, then refined at 8, 4 and 2 km. Prediction is done using clear sky factor or CSF. CSF = transmissivity / clear sky transmissivity CSF is derived from visible brightness using the techniques of Perez, et. at. (22) A bias correction is applied to CSF. Correction varies by solar zenith angle, cloud amount and time (before noon, after noon). A 7 hour forecast is produced at 15-minute intervals. PEREZ, R., P. INEICHEN, K. MOORE, M. KMIECEK, C. CHAIN, R. GEORGE and F. VIGNOLA, 22: A new operational model for satellite-derived irradiances: Description and validation. Solar Energy, 73,
9 Experiment Details 363 Day Training Period 3 December November 213. Two forecast periods: January 213, August 213. The period from 7 hours before to 2 weeks afer each forecast \me is excluded from its training sample. Forecast loca\ons: Sample of electric substa\ons with substan\al roofop PV. Surface irradiance observa\ons. Verifica\on Variables: Satellite- es\mated irradiance. Observed irradiance. Both converted to CSF. Substation Irradiance observation Forecast Sites
10 Choice of Case Matching Variables Mean over a 1 km box centered on the forecast site. CSF (CSF MEAN). Cloud Displacement vector amplitude (DISPAMP). Cloud displacement vector direc\on (DISPDIR). Standard devia\on of CSF (2 km box) (CSF STDEV). Mean cloud X and Y displacement over a larger area 5-1 km upstream in the prevailing east- northeasterly flow (DISPXY). Time of day As a case matching variable (TMATCH). As a regime variable (limit ensemble members to those within a certain \me window) (TWIN).
11 Choice of Case Matching Variables Baseline Forecasts JAN AUG 5 5 MAE (CSF) imagematch perstistence climotrend.5.5 Mean MAE of ensemble 5% POE over all sites, \mes vs. satellite es\mated CSF.
12 Choice of Case Matching Variables Single Variables JAN AUG 5 5 MAE (CSF) perstistence DISPAMP DISPDIR DISPXY CSF STDEV CSF MEAN.5.5 Mean MAE of ensemble 5% POE over all sites, \mes vs. satellite es\mated CSF.
13 Choice of Case Matching Variables 3 Variables - Time is Case Matching Variable JAN AUG 5 5 persistence DISPAMP MAE (CSF) DISPDIR DISPXY CSF STDEV CSF MEAN.5.5 CSF DISPAMP DISPDIR TMATCH Mean MAE of ensemble 5% POE over all sites, \mes vs. satellite es\mated CSF.
14 Choice of Case Matching Variables 3 Variables - Time is Regime Variable JAN AUG 5 5 persistence DISPAMP MAE (CSF) DISPDIR DISPXY CSF STDEV CSF MEAN CSF DISPAMP DISPDIR TMATCH CSF DISPAMP DISPDIR TWIN Mean MAE of ensemble 5% POE over all sites, \mes vs. satellite es\mated CSF.
15 Choice of Case Matching Variables Baseline JAN AUG MAE (CSF) imagematch persistence climotrend Mean MAE of ensemble 5% POE over all sites, \mes vs. 7 surface observa\ons.
16 Choice of Case Matching Variables Single Variables JAN AUG MAE (CSF) persistence DISPAMP DISPDIR DISPXY CSF STDEV CSF MEAN Mean MAE of ensemble 5% POE over all sites, \mes vs. 7 surface observa\ons
17 Choice of Case Matching Variables 3 Variables - Time is Case Matching Variable JAN AUG MAE (CSF) persistence DISPAMP DISPDIR DISPXY CSF STDEV CSF MEAN CSF DISPAMP DISPDIR TMATCH Mean MAE of ensemble 5% POE over all sites, \mes vs. 7 surface observa\ons.
18 Choice of Case Matching Variables 3 Variables - Time is Regime Variable JAN AUG MAE (CSF) persistence DISPAMP DISPDIR DISPXY CSF STDEV CSF MEAN.5.5 CSF DISPAMP DISPDIR TMATCH CSF DISPAMP DISPDIR TWIN Mean MAE of ensemble 5% POE over all sites, \mes vs. 7 surface observa\ons
19 Performance By Island Skill Score vs. Persistence. JAN AUG Skill vs. Persistence hawaii maui oahu Skill is higher in August, especially for Maui
20 Performance by Time of Day Skill Score vs. Persistence at Different forecast Look ahead \mes for 7 surface observa\ons Skillful Jan 1: forecast JAN AUG Persistence forecast for 17: is poor.4 Skill vs. Persistence Forecast Issue Hour (HST) Forecast Issue Hour (HST)
21 Main Points An analog ensemble technique was applied as a bias correc\on tool for a pyramidal image matcher based solar irradiance forecast.. Verifica\on Results over 2 months showed significant reduc\on of error over the raw PIM and persistence forecasts. Error reduc\on was more significant at some \mes of day. Future Work Improve forecast skill at around solar noon. Add frequent update NWP- derived variables to the case- matching variables. Test the analog ensemble s u\lity as a probabilis\c forecast tool. Apply the technique to other loca\ons.
22 Ques/ons?
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