Long term irradiance clear sky and all-weather model validation

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Long term irradiance clear sky and all-weather model validation

INEICHEN, Pierre

Abstract

The optimal utilization of solar energy requires a thorough characterization of the solar resource. The most accurate way is to measure that resource in situ. However accurate measurements are not a common commodity, especially over longer time spans. To circumvent the lack of ground-based measurements, models can be applied to estimate solar irradiance components. A fundamental component is the clear sky irradiance. In particular, clear sky irradiance is used as the normalization function in models that convert meteorological satellite images into irradiance. This paper presents the results of a validation of models that evaluate solar irradiance based on satellite images spanning up to 8 years.

The validation relies on high quality measurements from 24 sites in Europe and Africa. After a thorough assessment of the ground measurements, clear conditions are selected in the data banks to evaluate the performance of seven clear sky models. Finally, seven satellite all-weather models are validated against the ground data.

INEICHEN, Pierre. Long term irradiance clear sky and all-weather model validation. In: SASEC 2016. 2016.

Available at:

http://archive-ouverte.unige.ch/unige:88493

Disclaimer: layout of this document may differ from the published version.

Long term irradiance clear sky and all-weather model validation

Dr Pierre Ineichen

University of Geneva – Institute of Environmental Sciences

Solar resource: from ground to bankable data

Statement #1

ï it is illusory to develop and to validate solar resource models without high quality ground measurements Statement #2

ï high quality bankable data go through a stringent process of acquisition and quality control of the data Statement #3

ï high quality modeled data are based on the

knowledge of precise input parameters with adapted space and time granularity, mainly atmospheric

aerosol content and water vapor column

Solar resource assessment

Ground measurements

ï situation and site characterization

ï type of sensors, calibration and characterization Data archiving

ï measurements continuity, gap filling

ï data quality control (on line and long term)

ï format, Meta data, dissemination Modeling

ï satellite data (Meteosat, Goes, etc. images)

ï input parameters (turbidity, water vapor, etc.)

ï granularity (terrain aggregation, aod, w)

ï components (clear sky, beam, tilted, etc.) Model validation

ï ground data (climate, latitude, altitude, etc)

ï quality control (ground & model)

ï comparison statistics (first & second order)

ï results presentation

Validation background

ð 24 ground sites, in Europe and Mediterranean region, Skukuza & Stellenbosch

ð latitude: 20°-> 60°and South Africa, altitude: 0m -> 1600m, various climates

ð validation over up to 8 years, global, diffuse and normal beam components (Skukuza, only the global component available)

ð hourly, daily and monthly values comparison

ð aodinput data: MACC project, aeronet and Molineaux-Ineichen (bmpi retrofit model)

ð clear sky models: CAMS McClear, Solis, REST2, CPCR2, Bird, ESRA and Kasten

ð all-weather models: 14 global, 9 beam irradiance, average (8) and real time (6)

ð Interannual variability

Model validation

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Almeria (Spain) Bratislava (Slovakia) Cabauw (the Netherlands) Carpentras (France) Davos (Switzerland) Geneva (Switzerland) Kassel (Germany) Mt Kenya (Kenya) Kishinev (Moldavia) Lerwick (Great Britain) Lindenberg (Germany) Madrid (Spain) Nantes (France) Payerne (Switzerland) Sede Boqer (Israel) Skukuza (South Africa) Tamanrasset (Algeria) Toravere (Estonia) Valentia (Ireland) Vaulx-en-Velin (France) Wien (Austria) Zilani (Letonia)

Data quality control

Time stamp validation (acquisition time)

ï solar time symmetry (irradiance or clearness index K_t) Data absolute calibration

ï comparison with ancillary data (aeronet, nearby site, etc.)

ï year to year comparison (stability) Components coherence

ï 3 components: «closure equation»: global = direct + diffuse

ï 2 components: coherence with Solis clear sky model

Validation statistics

ð mean bias difference (mbd)

ð mean absolute bias difference (mabd)

ð root mean square difference (rmsd)

ð standard deviation (sd)

ð correlation coefficient (R or R²)

ð Kolmogorov-Smirnov integral (KSI)

ð standard deviation of the biases Including the dispersion induced by:

ð ground measurements uncertainty

ð comparison period length

ð algorithms precision

ð quality of the input data (aod, w, etc.)

ð comparison of data with different time/space granularities

Validation statistics

𝑚𝑏𝑑 = ∑(𝐺_𝑠𝑎𝑡 − 𝐺_𝑚𝑒𝑠) 𝑁

𝑟𝑚𝑠𝑑 = ' ∑(𝐺_𝑠𝑎𝑡 − 𝐺_𝑚𝑒𝑠)² 𝑁

𝑠𝑑 = %∑(𝐺_𝑠𝑎𝑡 − 𝐺_𝑚𝑒𝑠)² 𝑁

𝑅 = ∑%𝐺_𝑠𝑎𝑡 − 𝐺_𝑠𝑎𝑡 +%𝐺_𝑚𝑒𝑠 − 𝐺_𝑚𝑒𝑠+ .(∑(𝐺_𝑠𝑎𝑡− 𝐺_𝑠𝑎𝑡)²)(∑(𝐺_𝑚𝑒𝑠 − 𝐺_{𝑚𝑒 𝑠})²)

𝐾𝑆𝐼 = &^{𝐺𝑚𝑎𝑥}|𝐹_𝑐(𝐺_𝑠𝑎𝑡) − 𝐹_𝑐(𝐺_𝑚𝑒𝑠)| ∙ 𝑑𝐺_𝑚𝑒𝑠

𝐺𝑚𝑖𝑛

Results presentation

ð scatter plots (G_h, D_h, B_n)

ð bias dependence (sky type, aod)

ð clearness index versus solar elevation

ð frequency distribution

ï irradiance, K_t, cumulated

ï bias around the 1:1 axis

ð Comparison of monthly values: seasonal dependence

ð tables, histograms, etc.

Results presentation

From MACC project

ð retrieved by MinesParisTech,

From ground measurements: Molineaux-Ineichen bmpi model

ð Integrated Modtran & smarts2 RTM calculations

ð D_cda = -0.101 + 0.235 am_R^-0.16 D_w = 0.112 am_R^-0.55w ^0.34

ð dailyaodretro-calculated from DNI & GHI with Solis From Aeronet network (spectral ground measurements)

ð level 2.0 (if not available, level 1.5) in daily values Comparison

ð Solis retrofit/aeronetaod(daily values): 5% bias, good correlation (both issued from ground measurements)

ð Macc/aeronet (daily values): high dispersion and bias

ð w(Ta,HR)/aeronet: no bias, high dispersion, low impact on the models results

Input data

Overall results (hourly values)

ð negligible bias

ð GHI: sd = 2.5% - 3%

ð DNI: sd = 3% - 10%

Standard deviation of the bias

ð GHI: ∼20 W/m² 3.5%

ð DNI: ∼25 - 39 W/m² 3% - 5%

ð DIF: ∼25 W/m² 25%

South Africa results

ð Stellenbosch

ð GHI: 1.8% or 13 W/m²

ð DNI: 4.5% or 41 W/m²

ð Skukuza

ð GHI: 1.7 – 4.5%

Clear sky model validation

Bias dependence (Stellenbosch)

ð dependence with the aerosol optical depth

ð similar to other models and sites

ð no specific seasonal dependence

ð the distribution of the bias around the 1:1 axis is near of normal (except for Davos and Mt Kenya) -> first order statistic reliable

ð the highest beam measurements are never reached by the modelled values whatever the model and the site are

Clear sky model validation

Overall validation results:

í hourly: no bias, sd(G_h) = 17-20%, bias 2%, sd(B_n) = 34-50%

í daily: no bias, sd(G_h) = 8-12%, bias 2%, sd(B_n) = 20-32%

í monthly: no bias, sd(G_h) = 3-6%, bias 2%, sd(B_n) = 9-17%

Stellenbosch

í hourly neg. bias, sd(G_h) = 13%, bias -3%, sd(B_n) = 21%

mbd sd mbd sd mbd sd mbd sd mbd sd mbd sd mbd sd mbd sd mbd sd

0 57 -6 119 7 47 5 70 21 166 -13 60 6 80 -40 170 33 80

0% 17% -2% 34% 5% 35% 1% 20% 6% 47% -10% 45% 2% 23% -11% 49% 25% 60%

0.00 0.29 -0.05 0.75 0.07 0.32 0.05 0.44 0.22 1.25 -0.14 0.46 0.07 0.49 -0.39 1.23 0.33 0.62 0% 8% -1% 20% 5% 22% 1% 12% 6% 32% -9% 31% 2% 13% -10% 32% 23% 42%

-0.1 3.6 -1.7 10.4 2.3 4.5 1.5 7.5 6.6 18.6 -4.1 6.1 2.0 6.2 -12.6 16.6 10.5 9.6 0% 3% -2% 9% 5% 11% 1% 7% 6% 17% -10% 14% 2% 6% -11% 15% 25% 23%

bias sd

mbd sd mbd sd mbd sd mbd sd mbd sd mbd sd mbd sd mbd sd mbd sd

4 66 0 140 5 55 0 75 7 165 2 55 2 81 -1 174 5 62

1% 19% 0% 39% 4% 41% 0% 22% 2% 47% 2% 42% 1% 24% 0% 49% 4% 46%

0.04 0.33 0.02 0.90 0.05 0.39 0.00 0.43 0.07 1.10 0.03 0.39 0.02 0.44 0.00 1.17 0.04 0.41

1% 9% 0% 23% 3% 27% 0% 12% 2% 30% 2% 28% 0% 12% 0% 31% 3% 30%

1.1 4.2 0.3 12.1 1.6 5.2 0.1 5.0 2.0 11.7 0.8 4.8 0.6 6.4 -0.3 15.8 1.6 5.3

1% 4% 0% 11% 4% 12% 0% 5% 2% 11% 2% 12% 1% 6% 0% 15% 4% 13%

SolarGis Helioclim 3 Solemi

Heliomont

Gh Bn Dh Gh Bn Dh Gh Bn Dh

131 340

Dh Gh Bn Dh

134

1.46 hourly

[Wh/m2h]

341 351 134 345 354 134 342 350

346 354

CM-SAF IrSOLaV

Gh Bn Dh Gh

3.73 1.38 3.60 3.73 1.41

353 134

3.75 3.85 1.47 3.56

135 337 354

Bn Daily

[kWh/m2]

3.73 3.83 1.46 3.81 3.91 1.49 3.73 3.82

40.5 104.3 107.9 40.8

hourly [Wh/m2h]

Daily [kWh/m2]

Monthly [kWh/m2]

107.6 110.3 42.1 104.2 109.4

112.1 42.8 108.8 111.3 42.5

Monthly [kWh/m2]

108.5 111.4 42.5 109.3

2.1% 5.9% 7.5% 5.1% 13.9% 14.2% 4.8% 14.5% 25.2%

-60%

-40%

-20%

0%

20%

40%

60%

80%

Global irradiation Beam irradiation Diffuse irradiation SolarGis Helioclim 3 Solemi Heliomont CM-SAF IrSOLaV

hourly monthlydaily

All-weather model validation

Validation results (Stellenbosch)

ð G_h: near normal bias distribution around the 1:1 axis -> reliable statistics

ð B_n: non normal high dispersion, the standard deviation

ð similar behavior for all the models with the clearness index K_t

ð Model/measurements comparable monthly dispersion

Validation results

Validation results (Stellenbosch)

ð coherent frequency distribution for the global irradiance

ð different behavior in hourly and daily values

ð different behavior depending on the component

ð no particular seasonal effect

Validation results

Other sites and models behavior

ð helioclim 3 v4: poor clear sky model taken into account in the final model, corrected in hc3 v5

ð better results with daily input parameters instead of monthly climatic data banks

ð Better snow management in Heliomont and SolarGis

Validation results

Bankable solar resource

ð Long term measurements

ð Various lat/long/alt, climate, etc.

ð Stringent quality control

ð Model development and validation

Dynamic models’ uncertainty (hourly values)

ð Clear sky model

ð negligible bias

ð G_h : 2.5% - 3%

ð B_n: 3% - 10%

ð

ð All-weather model

ð G_h: 17 - 20%, no bias

ð B_n: 34 - 50%, 2% bias

Conclusions

Publications : http://www.unige.ch/energie/fr/equipe/ineichen/

Thank you for your attention