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

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

Who we are 3 RSE ( 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 ( 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.

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

Post-processing  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. Kylling et al.,  use some different approaches to manage model liquid/ice water content Evaluating solar irradiances by means of a post-processing process makes it possible to: ExpNWPRadiative schemeCloud scheme EAHLAMIGeleyn-Hollingsworth OPELAMIGeleyn-Hollingsworth EMODLAMIGeleyn-HollingsworthModel Qi, Qc, CLC KATOLAMIKato2Model Qi, Qc, CLC RAMS Ritter-GeleynModel Qi, Qc, CLC REAHRAMSGeleyn-HollingsworthAs EAH 5

Global Irradiance in clear sky conditions 6 MilanoCasaccia

Daily global irradiance - Milano 7

Hourly global horizontal irradiance – Milano – –

Diffuse component: diffuse fraction (D h /G h ) vs. clearness index (G h /G 0h ) CasacciaMilano Blue line after Ruiz-Arias, Alsamarra, Tovar -Pescador, Pozo-Vasquez,

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

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

Model Output Statistic Training period:  Forecast period:  Applied to global and diffuse components R software – lm (glm) – Correlated observed irradiance with forecasted irradiance, solar altitude forecasted precipitable water content 12

Global component – Milano and Casaccia – red after MOS 13

Diffuse component – Milano and Casaccia – red after MOS 14

BIAS improvement after MOS 15

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

Thank you 17