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Experiences with MOS technique applied to a solar radiation forecast system.

ECAM - EMS Berlin, 12 -16 September 2011. Experiences with MOS technique applied to a solar radiation forecast system. D. Ronzio , P. Bonelli. Outline. RSE solar forecast system Validation (03/2010  08/2011) Model Output Statistic Conclusions. Who we are.

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Experiences with MOS technique applied to a solar radiation forecast system.

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  1. ECAM - EMS Berlin, 12 -16 September 2011 Experiences with MOS technique applied to a solar radiation forecast system. D. Ronzio , P. Bonelli

  2. Outline RSE solar forecast system Validation (03/2010  08/2011) Model Output Statistic Conclusions

  3. Whowe are RSE (www.rse-web.it) carries out research into the field of electrical energy with special focus on national strategic projects funded through the Government Fund for Research into Electrical Systems. RSE is a total publicly-controlled Company: the sole shareholder is GSE S.p.A (www.gse.it). The activity covers the entire supply system with an application-oriented, experimental and system-based approach. The activities of our group concern: • application of meteorological modeling to the assessment of renewable energy capability; • forecast of the meteorological variables influencing short and long term management of the electric system; • experimental and model studies on the main phenomena influencing the grid safety; • climatic change and their impacts on the electro-energy system.; • application of meteorological and chemical modeling for the assessment of the electric system impact on the air quality.

  4. RSE radiation forecast system LAM Models: LAMI (ARPA EMR) RAMS (RSE) Variables: pres, temp, rhu, liquid/ice water content, cloud cover +72 h (1h step) Global Model ECMWF/GFS Global, Diffuse, DNI horizontal irradiance RTM: Radiative Transfer Model Cloud scheme choice Measurements (MLN, CSC, CTN) Model Output Statistic for global and diffuse irradiance

  5. Post-processing Evaluating solar irradiances by means of a post-processing process makes it possible to: • evaluate some particular variables, such as DNI, generally not included into native NWP output lists; • use and compare different radiative schemes: • Geleyn-Hollingworth (our RTM), Ritter-Geleyn (LAMI, RAMS); • Kato [from LibRadTran, B. Mayer, A. Kyllinget al. , http://www.libradtran.org] • use some different approaches to manage model liquid/ice water content

  6. Global Irradiance in clear sky conditions Milano Casaccia

  7. Daily global irradiance - Milano

  8. Hourly global horizontalirradiance – Milano – 2010-03-01 – 2011-08-31

  9. Diffuse component: diffuse fraction (Dh/Gh) vs. clearness index (Gh/G0h) Casaccia Milano BluelineafterRuiz-Arias, Alsamarra, Tovar-Pescador, Pozo-Vasquez, 2010

  10. Improvement Improvement of cloud schemes evaluated by means of: where SCORE stands for RMSE or MAE

  11. Daily cumulative relative indexes (BIAS, RMSE, MAE) - Milano

  12. Model Output Statistic • Training period: 2010-03-01  2011-02-28 • Forecast period: 2011-03-01  2011-08-31 • Applied to global and diffuse components • R software • lm (glm) • Correlated observed irradiance with • forecasted irradiance, • solar altitude • forecasted precipitable water content

  13. Global component – Milano and Casaccia – red after MOS

  14. Diffuse component – Milano and Casaccia – red after MOS

  15. BIAS improvement after MOS

  16. A few conclusions • We have analyzed some radiative transfer models (G-H, R-G, Kato2) and clouds representations (native, function of RH), obtaining good performances for all the three Italian sites, with RMSE about 25-30% and MAE 15-20%. Improvement in RMSE of about 30% respect to persistence has been obtained. • The application of a Model Output Statistic reduce BIAS from -5÷15% to about 1-1.6% for the global irradiance, and of about 8% even the absolute errors for the diffuse component. • Kato (LibRadTran) scheme has been used without considering the cloud fraction (no IPA), but only the cloud water content, and so there is room to get better results. • A lot of information can be extracted from NWP microphysics (mixing ratio of several hydrometeors and their effective diameters) but also vertical fraction cloud cover has to be managed • Native short wave component from RAMS is non satisfying, but the use of its microphysics information is non straightforward and more work has to be done yet. Acknowledgements: This work has been financed by the Research Fund for the Italian Electrical System under the Contract agreement between RSE (formerly known as ERSE) and the Ministry of Economic Development – General Directorate for Nuclear Energy, Renewable Energy and Energy Efficiency stipulated on July 29, 2009 in compliance with the Decree of March 19, 2009

  17. Thank you

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