Short- term solar forecas/ng with sta/s/cal models and combina/on of models
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1 Short- term solar forecas/ng with sta/s/cal models and combina/on of models Philippe Lauret, Mathieu David PIMENT Laboratory, University of La Réunion L. Mazorra Aguiar University of Las Palmas de Gran Canaria ISES Solar Resource Forecasting Webminar January,
2 Forecas+ng methods CM- Sat: Cloud- mo/on based on satellite images NWP: Numerical Weather Predic/on model CM- SI: Cloud- mo/on based on sky- imagers machine learning techniques Figure from Best Prac/ces Handbook for the Collec/on and Use of Solar Resource Data for Solar Energy Applica/ons, Tech. Report, NREL/ TP- 5D Available online:hwp:// ISES Solar Resource Forecasting Webminar January,
3 Use of sta+s+cal models for intra- day solar forecas+ng Focus on intra- day solar forecas/ng i.e predic/ons of the solar irradiance from 1h up to 6 hours in the future machine learning techniques 1h 6h Forecast horizon ISES Solar Resource Forecasting Webminar January,
4 Outline of the presenta+on Past ground measurements of GHI 1 1 GHI forecasts GHI (W.m 2) Ground observation 5 1 hour Forecas/ng model GHI (W.m 2) Ground observation Forecast GHI: Global Horizontal Irradiance 5 1 hour ISES Solar Resource Forecasting Webminar January,
5 Outline of the presenta+on Past ground measurements of GHI 1 GHI forecasts GHI (W.m 2) Ground observation 5 1 hour Forecas/ng model GHI (W.m 2) Ground observation Forecast 5 1 hour Forecasting Error ISES Solar Resource Forecasting Webminar January,
6 Outline of the presenta+on Past ground measurements of GHI GHI (W.m 2) Ground observation Forecas/ng model GHI (W.m 2) GHI forecasts Ground observation Forecast 5 1 hour 5 1 hour 45 Forecasting Error 35 Ground Ground+NWP 1 ISES Solar Resource Forecasting Webminar January,
7 Outline of the presenta+on 1 Ground observation 1 GHI (W.m 2) hour GHI forecasts Forecas/ng model 1 Ground observation Forecast 1 8 GHI (W.m 2) Past ground measurements of GHI hour 45 Satellite data Forecasting Error 35 Ground Ground+NWP Ground+NWP+Satellite 1 1 ISES Solar Resource Forecasting Webminar January,
8 Sites for intra-day solar forecasting 2 stations from the SURFRAD network: Desert Rock and Fort Peck SURFRAD Network, Monitoring Surface Radiation in the Continental United States. Available online: ISES Solar Resource Forecasting Webminar January,
9 Sites for intra-day solar forecasting 3 insular sites: La Réunion island (2) and Gran Canaria island ISES Solar Resource Forecasting Webminar January,
10 Data processing Clear sky model Rayonnement global GHI (W/m2) Clear sky index: hour Heure! =!"#!"#!"#$% 1 Clear sky kt* k*.5 Cloudy Overcast Heure Forecasting models are fitted to the clear sky index time series ISES Solar Resource Forecasting Webminar January,
11 Sky conditions analysis 7 6 Saint Pierre Le Tampon Desert Rock Fort Peck % Clear sky index Solar variability intermediate high low intermediate ISES Solar Resource Forecasting Webminar January,
12 Benchmarking of the forecasting models 2 references models: Persistence and climatological mean 1 /me series model model (ARMA model) with recursive es/ma/on of the parameters 1 machine learning technique (Ar/ficial Neural Network) Evalua/on of the forecas/ng performance :!"#$ = 1!!!!!!"#!"#$!"#$.!!"#!"#$%&"'.!! Forecast skill parameter:! % = 1!"#$!"#!!"!"#$!"#$ 1 ISES Solar Resource Forecasting Webminar January,
13 Results - Reference models Saint Pierre 7 Le Tampon Desert Rock Fort Peck !! + h =!"#!!!!! + h =!! ISES Solar Resource Forecasting Webminar January,
14 Results - Reference models Saint Pierre 7 Le Tampon Desert Rock Fort Peck !! + h =!"#!!!!! + h =!! ISES Solar Resource Forecasting Webminar January,
15 Forecasting methodology with time series model Past ground measurement values of k* Forecasted k* for /me horizon h k*(t) k*(t- 1) k*(t- 2) Forecasting model k*(t+h), h=1,2,3, Ground observation Forecast GHI (W.m 2) hour ISES Solar Resource Forecasting Webminar January, 27-16
16 Results Use of ground data only Saint Pierre 7 Le Tampon Desert Rock Fort Peck ISES Solar Resource Forecasting Webminar January,
17 Use of exogenous inputs provided by NWP models GHI and TCC forecasts provided by the European Centre for Medium Range Weather Forecasts (ECMWF) o o o o o IFS model Generated at 12h UTC (16h in Reunion) Available at 18h55 UTC (22h55 in reunion) Spatial resolution:.1 x.1 ( 14 x 14 km near Reunion) Temporal resolution: 1 hour ISES Solar Resource Forecasting Webminar January,
18 Results Combination of Ground and ECMWF data 45 Saint Pierre 7 Le Tampon 35 6 ISES Solar Resource Forecasting Webminar January,
19 Results Combination of Ground and ECMWF data ARMA Rls ANN GROUND+ECMWF Saint Pierre Forecast Skill (%) 1 Tampon 1 ü higher values of s-skill scores are obtained with the combination of on-site measured irradiance and NWP model output ISES Solar Resource Forecasting Webminar January,
20 Exogenous inputs provided by Satellite data Satellite-derived GHI for Gran Canaria obtained from HelioClim solar radiation databases o o ISES Solar Resource Forecasting Webminar Spatial resolution: 3 km x 3km Temporal resolution: mins January, 27-16
21 Selection of Satellite pixels Selec/on of satellite pixels by calcula/ng the cross- correla/on between ground clear sky index and satellite clear sky indices:!! (!,!)! =!"##!!"#$%&!,!!"#$%%&#$!!!!!!"#!!!! =!,!,!!&!! ISES Solar Resource Forecasting Webminar January,
22 Combination of Ground, ECMWF and Satellite data Forecast Skill (%) 35 GROUND+ECMWF GROUND+Satellite GROUND+ECMWF+SAT Las Palmas 1 5 ü Overall, the forecast skill of the combination GROUND+ECMWF+SAT is better than the forecast skill of the two other models GROUND+ECMWF and GROUND+SAT ISES Solar Resource Forecasting Webminar January,
23 Conclusions and further investigations ü Use of exogenous inputs (such as those provided by NWP models or Satellite data) improve the accuracy of the intra-day solar forecasts. Further investigations: u Use of other input paramters e.g. wind fields, humidity, cloud height provided by a NWP model u Implementation of ECMWF forecasts in an ARMAX Recursive Least Square model ISES Solar Resource Forecasting Webminar January,
24 Further readings: Use of exogenous inputs in forecasting models: A. Zagouras, H. Pedro and C. Coimbra, On the role of lagged exogenous variables and spatiotemporal correlations in improving the accuracy of solar forecasting methods, Renewable Energy, vol. 78, pp. 3-18,. B. Marquez, R., Coimbra, C.F.M., 11. Forecasting of global and direct solar irradiance using stochastic-learning methods, ground experiments and the NWS database. Solar Energy 85, C. R. Dambreville, P. Blanc, J. Chanussot and D. Boldo, Very short term forecasting of the Global Horizontal Irradiance using a spatio-temporal autoregresive model, Renewable Energy, vol. 72, pp. 291-, 14. D. C.Voyant, M. Muselli, C. Paoli and M.L Nivet, «Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation, Energy, Energy, Volume 39, Issue 1, March 12, Pages E. L. Mazorra Aguiar, B. Pereira, M. David, F. Diaz and P. Lauret, «Use of satellite data to improve solar radiation forecasting with Bayesian Artificial Neural Networks,» Solar Energy, vol. 122, pp ,. F. Bacher, P.; Madsen, H.; Nielsen, H.A. (9). Online Short-Term Solar Power Forecasting. Solar Energy (83); pp. 1,772 1,783. G. P. Lauret, E. Lorenz, M. David. «Solar Forecasting in a Challenging Insular Context» accepted for publication in Atmosphere journal, ISSN , ISES Solar Resource Forecasting Webminar January,
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