Experiences with MOS technique applied to a solar radiation forecast

system. D. Ronzio , P. Bonelli

ECAM - EMS Berlin, 12 -16 September 2011

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

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Outline

Who we are

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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.

Global, Diffuse, DNI

horizontal irradiance

RTM: RadiativeTransfer Model

Variables:pres, temp, rhu,

liquid/ice water content,

cloud cover+72 h (1h step)

LAM Models:LAMI (ARPA EMR)

RAMS (RSE)

Global ModelECMWF/GFS

RSE radiation forecast system

Cloud scheme choice

Model Output Statistic for global and diffuse irradiance

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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. , http://www.libradtran.org]

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:

Exp NWP Radiative scheme Cloud schemeEAH LAMI Geleyn-Hollingsworth

OPE LAMI Geleyn-Hollingsworth

EMOD LAMI Geleyn-Hollingsworth Model Qi, Qc, CLC

KATO LAMI Kato2 Model Qi, Qc, CLC

RAMS RAMS Ritter-Geleyn Model Qi, Qc, CLC

REAH RAMS Geleyn-Hollingsworth As EAH

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)(1)1(h-1FHUC1

FHUC-RHCLC

kFHUC

)(1hexp-1FHUC

1FHUC-RHCLC

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kFHUC

Global Irradiance in clear sky conditions

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Milano Casaccia

Daily global irradiance - Milano

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Hourly global horizontal irradiance – Milano – 2010-03-01 – 2011-08-31

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Diffuse component: diffuse fraction (Dh/Gh) vs. clearness index (Gh/G0h)

Casaccia Milano

Blue line after Ruiz-Arias, Alsamarra, Tovar -Pescador, Pozo-Vasquez, 20109

Improvement

Improvement of cloud schemes evaluated by means of:

110

01

coscos

t

t

t

ttt

IIOBSPERS

pers

csperscs SCORE

SCORESCOREGain

where SCORE stands for RMSE or MAE

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EAH OPE EMOD KATO PERS0

5

10

15

20

25

30

35

40 Milano - Relative RMSE +24h, +48h, +72h

pRMSE+24pRMSE+48pRMSE+72

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

EAH OPE EMOD KATO PERS

-10

-5

0

5

10

15

20

Milano - Relative BIAS - +24h, +48h, +72h

pBIAS+24pBIAS+48pBIAS+72

EAH OPE EMOD KATO PERS0

5

10

15

20

25

30

Milano - Relative MAE +24h, +48h, +72h

pMAE+24pMAE+48pMAE+72

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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

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Global component – Milano and Casaccia – red after MOS

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Diffuse component – Milano and Casaccia – red after MOS

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BIAS improvement after MOS

MLN-orig MLN-mos CSC-orig CSC-mos CTN-orig CTN-mos

-10

-5

0

5

10

15

20

25

BIAS (Milano, Casaccia, Catania) - MOS

EAHOPEEMODKATORAMSREAH

%

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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

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