Soleksat. a flexible solar irradiance forecasting tool using satellite images and geographic web-services

Transcription

1 Soleksat a flexible solar irradiance forecasting tool using satellite images and geographic web-services Sylvain Cros, Mathieu Turpin, Caroline Lallemand, Quentin Verspieren, Nicolas Schmutz They support us:

2 The company

3 Reuniwatt, a softgrid startup Create value, acquire a technical legitimacy in renewable energies and information and communication technologies (ICT) Three structuring activities: Energy mix expertise: Reuniwatt s first activity in Indian Ocean area: energy projects consultancy Climatic Information Systems: On-site instrumentation and environmental geo-database management Soleka, solar power production forecasting: Development of state-of-the-art methods to predict the solar power production for energy stakeholders

4 An innovation-oriented team 3 Young Doctors: Atmospheric physics Meteorology Artificial Intelligence 1 MBA 2 Masters degree Meteorology Building physics 10 engineers: Information technology Computer vision Statistics Electrical engineering Innovation management

5 Energy forecasting legitimacy The scientific team has been at the cutting edge of solar power forecasting for 10 years More than 20 publications (peer-review, international conference communication) Reuniwatt has won 7 prizes and awards for its Soleka project since Member of the IEA SHC Task 46 Partner of IssyGrid, first French operational smart-grid

6 Soleka, our forecasting tool

7 Soleka, a technology mix Time series modeling (ARMA, SVM, ANN) Regional NWP forecast Cloud tracking using all sky camera Cloud tracking using GEO satellite

8 Soleksat, the satellite and geoinformation expertise of Soleka

9 Overview of Soleksat: GHI estimation Meteosat-9 raw image (June 6th 2012, 1200 UTC) Cloud index: comparison between actual and clear sky reflectance for a given pixel GHI: global horizontal irradiation at ground level Heliosat-2 method (Rigollier et al., 2004; Cros 2004) with modifications: Ground albedo derived from MODIS land surface data (Blanc et al., 2014) Independant clear sky model (libradtran base) for atmospheric reflectance modeling and GHI computation from clear sky index)

10 Overview of Soleksat: GHI forecast Optical flow analysis by Lucas-Kanade method T0 15 min. Cloud index map Cloud motion vector extraction T0 Cloud index map Lucas and Kanade (1981) Cros et al. (2014) Cloud motion vector field on T0 HRV map

11 Cloud index extrapolation Original T0 Cloud Index map Forecasted Cloud index maps Up to 6 hours (step 15 min)

12 Cloud index image filtering Spatial smoothing filter reduces significantly the forecast error. A rectangular filter is applied on each extrapolated cloud index images. (Lorenz et al., 2004) Rectangle height (pixel) Choosing the filter size minimizing the RMSE between - predicted cloud index map - actual cloud index map Optimal size of rectangular filter increases with time horizon prediction (blue curve: t+1h ; pink curve: t+5h)

13 Forecast quality assessment over two BSRN stations Comparisons with pyranometer measurements (Year 2013) 2 BSRN sites in France Palaiseau (lat : ; lon: 2.21 ) Relative RMSE (%) Carpentras (lat: ; lon: 5.06 ) (15 min. 80 time step) Soleksat Persistence Time horizon (min.) Time horizon (min.)

14 Carpentras : H+1 Forecast Persistence RMSE (%): 23.2 Bias (%): -4.4 Corr. coef.: 0.95 RMSE (%): 28.1 Bias (%): -5.6 Corr. coef.: 0.93

15 Carpentras : H+2 Forecast Persistence RMSE (%): 27.8 Bias (%): -2,8 Corr. coef.: 0.95 RMSE (%): 33.4 Bias (%): -6.0 Corr. coef.: 0.92

16 Carpentras : H+3 Forecast Persistence RMSE (%): 31.3 Bias (%): -3.0 Corr. coef.: 0.94 RMSE (%): 36.7 Bias (%): -6.6 Corr. coef.: 0.91

17 Carpentras : H+4 Forecast Persistence RMSE (%): 34.9 Bias (%): -2.6 Corr. coef.: 0.94 RMSE (%): 40.7 Bias (%): -7.7 Corr. coef.: 0.91

18 Carpentras : H+5 Forecast Persistence RMSE (%): 39.1 Bias (%): -3.5 Corr. coef.: 0.94 RMSE (%): 45.2 Bias (%): -9.5 Corr. coef.: 0.90

19 Carpentras : H+6 Forecast Persistence RMSE (%): 46.0 Bias (%): Corr. coef.: 0.90 RMSE (%): 50.6 Bias (%): Corr. coef. (%): 0.89

20 Focus on April 8th 2013 in Carpentras

21 Focus on April 8th 2013 in Carpentras

22 Focus on April 8th 2013 in Carpentras

23 Focus on April 8th 2013 in Carpentras

24 Focus on April 8th 2013 in Carpentras

25 Focus on April 8th 2013 in Carpentras

26 Focus on April 8th 2013 in Carpentras

27 Focus on April 8th 2013 in Carpentras

28 Focus on April 8th 2013 in Carpentras

29 Focus on April 8th 2013 in Carpentras

30 Focus on April 8th 2013 in Carpentras

31 Focus on April 8th 2013 in Carpentras

32 Focus on April 8th 2013 in Carpentras

33 Focus on April 8th 2013 in Carpentras

34 Focus on April 8th 2013 in Carpentras

35 Focus on April 8th 2013 in Carpentras

36 Focus on April 8th 2013 in Carpentras

37 Focus on April 8th 2013 in Carpentras

38 Focus on April 8th 2013 in Carpentras

39 Focus on April 8th 2013 in Carpentras

40 Focus on April 8th 2013 in Carpentras

41 Focus on April 8th 2013 in Carpentras

42 Focus on April 8th 2013 in Carpentras

43 Focus on April 8th 2013 in Carpentras

44 Focus on April 8th 2013 in Carpentras GHI (W.m^ BSRN CIEL CLAIR PREV PERS PREV FILT Spatial smoothing reduces the RMSE but removes the GHI forecast variability

45 Geographic web-service

46 Geolocated information

47 An interoperable geographic webservice with M2M interaction Computing server Raw satellite image real-time reception GIS: Climatology Conversion in Geotiff format HTTP Request Cloud index map Satellite images archives Climate variable extraction for zones of interest Ground Albedo Atm. Param. (water vapor, aerosol) Cloud motion vector computation Cloud index extrapolation Irradiance assessment and forecast storage Terrain Elevation Atm. Parameters if available in real-time WCS Web Coverage Service External webservices E.G. Copernicus Big Data: Meteorology Forecast validation Forecast customers delivery Forecast results analysis

48 Conclusion & Perspectives

49 Short-term forecast using satellite images Benefits of Heliosat-2 working on any calibrated satellite irradiance Geographical web services using Open Geospatial Consortium standards is a real opportunity to encourage innovations with convenient access to new ancillary data. Lucas-Kanade algorithm provides a realistic extrapolation of cloud cover Limitation to cloud advection hypothesis Ongoing studies for improvements: Combining with NWP results for large horizons (4 to 6h) Using wind vectors derived from NWP Using satellite infrared channels for early morning forecast Bidimensionnal time-series modeling (e.g. autoregressive models, neural networks)

50 Bibliography Blanc, P., Gschwind, B., Lefevre, M., & Wald, L. (2014, July). Twelve monthly maps of ground albedo parameters derived from MODIS data sets. In Geoscience and Remote Sensing Symposium (IGARSS), 2014 IEEE International (pp ). IEEE. Cros S., Sébastien N., Liandrat O., Schmutz N., Cloud pattern prediction from geostationary meteorological satellite images for solar energy forecasting, Proc. SPIE 9242, Remote Sensing of Clouds and the Atmosphere XIX; and Optics in Atmospheric Propagation and Adaptive Systems XVII, (October 21, 2014); doi: / Cros S., Création d'une climatologie du rayonnement solaire en ondes courtes à partir d'images satellitales, PhD thesis, Ecole des Mines de Paris, Sophia Antipolis, 2004 Lorenz, E., Hammer, A., & Heinemann, D. (2004, June). Short term forecasting of solar radiation based on satellite data. In EUROSUN2004 (ISES Europe Solar Congress) (pp ). Lucas, B. D., & Kanade, T. (1981, August). An iterative image registration technique with an application to stereo vision. In IJCAI (Vol. 81, pp ). Rigollier, C., Lefèvre, M., & Wald, L. (2004). The method Heliosat-2 for deriving shortwave solar radiation from satellite images. Solar Energy, 77(2), Meteosat-9 and -10 images provided by

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