clear sky and all-weather global and beam irradiance models: long term validation
TRANSCRIPT
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
Clear sky and all-weather global and beam irradiance models: long term validation
Dr Pierre Ineichen
University of Geneva – Institute of Environmental Sciences
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
Solarresource:fromgroundtobankabledata
Statement#1ï itisillusorytodevelopandtovalidatesolarresource
modelswithouthighqualitygroundmeasurements
Statement#2ï highqualitybankabledatagothroughastringent
processofacquisitionandqualitycontrolofthedata
Statement#3ï highqualitymodeleddataarebasedonthe
knowledgeofpreciseinputparameterswithadaptedspaceandtimegranularity,mainlyatmosphericaerosolcontentandwatervaporcolumn
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
Solarresourceassessment
Groundmeasurementsï situationandsitecharacterizationï typeofsensors,calibrationandcharacterization
Dataarchivingï measurementscontinuity,gapfillingï dataqualitycontrol(onlineandlongterm)ï format,Metadata,dissemination
Modelingï satellitedata(Meteosat,Goes,etc.images)ï inputparameters(turbidity,watervapor,etc.)ï granularity(terrainaggregation,aod,w)ï components(clearsky,beam,tilted,etc.)
Modelvalidationï grounddata(climate,latitude,altitude,etc)ï qualitycontrol(ground&model)ï comparisonstatistics(first&secondorder)ï resultspresentation
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
Validationbackground
ð 22groundsites,inEuropeandMediterraneanregion,ð latitude:20° ->60°,altitude:0m->1600mð validationoverupto8years,global,diffuseandnormalbeamcomponents(when
onlyglobalcomponent,beamfromDirIndex)ð hourly,dailyandmonthly valuescomparisonð aod inputdata:MACCproject,aeronet andMolineaux-Ineichen(bmpi retrofit model)ð clearskymodels:CAMSMcClear,Solis,REST2,CPCR2,Bird,ESRAandKastenð all-weathermodels:14global,9beamirradiance,average(8)andrealtime(6)ð Interannualvariability
Modelvalidation
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(theNetherlands)Carpentras(France)Davos(Switzerland)Geneva(Switzerland)Kassel(Germany)MtKenya(Kenya)Kishinev(Moldavia)Lerwick(GreatBritain)Lindenberg(Germany)Madrid(Spain)Nantes(France)Payerne(Switzerland)SedeBoqer(Israel)Skukuza(SouthAfrica)Tamanrasset(Algeria)Toravere(Estonia)Valentia(Ireland)Vaulx-en-Velin(France)Wien(Austria)Zilani(Letonia)
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
Dataqualitycontrol
Timestampvalidation(acquisitiontime)ï solartimesymmetry(irradianceorclearnessindexKt)
Dataabsolutecalibrationï comparisonwithancillarydata(aeronet,nearbysite,etc.)ï yeartoyearcomparison(stability)
Componentscoherenceï 3components:«closureequation»:global=direct+diffuseï 2components:coherencewithSolisclearskymodel
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
Validationstatistics
ð meanbiasdifference(mbd)ð meanabsolutebiasdifference(mabd)ð rootmeansquaredifference(rmsd)ð standarddeviation(sd)ð correlationcoefficient(RorR2)ð Kolmogorov-Smirnovintegral(KSI)ð standarddeviationofthebiases
Includingthedispersioninducedby:
ð groundmeasurementsuncertaintyð comparisonperiodlengthð algorithmsprecisionð qualityoftheinputdata(aod,w,etc.)ð comparisonofdatawithdifferenttime/space
granularities
Validationstatistics
𝑚𝑏𝑑 = ∑(𝐺𝑠𝑎𝑡 − 𝐺𝑚𝑒𝑠 )
𝑁
𝑟𝑚𝑠𝑑 = '∑(𝐺𝑠𝑎𝑡 − 𝐺𝑚𝑒𝑠 )2
𝑁
𝑠𝑑 = %∑(𝐺𝑠𝑎𝑡 − 𝐺𝑚𝑒𝑠 )2
𝑁
𝑅 =∑%𝐺𝑠𝑎𝑡 −𝐺𝑠𝑎𝑡 +%𝐺𝑚𝑒𝑠 −𝐺𝑚𝑒𝑠 +
.(∑(𝐺𝑠𝑎𝑡 −𝐺𝑠𝑎𝑡 )2)(∑(𝐺𝑚𝑒𝑠 −𝐺𝑚𝑒𝑠)2)
𝐾𝑆𝐼 = & |𝐹𝑐(𝐺𝑠𝑎𝑡 ) −𝐹𝑐(𝐺𝑚𝑒𝑠 )| ∙ 𝑑𝐺𝑚𝑒𝑠𝐺𝑚𝑎𝑥
𝐺𝑚𝑖𝑛
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
Resultspresentation
ð scatterplots(Gh,Dh,Bn)
ð biasdependence(skytype,aod)
ð clearnessindexversussolarelevation
ð frequencydistribution
ï irradiance,Kt,cumulated
ï biasaroundthe1:1axis
ð Comparisonof monthlyvalues:seasonaldependence
ð tables,histograms,etc.
Resultspresentation
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
FromMACCprojectð retrievedbyMinesParisTech,
Fromgroundmeasurements:Molineaux-Ineichenbmpimodelð IntegratedModtran &smarts2RTMcalculationð Dcda =-0.101+0.235amR
-0.16 Dw =0.112amR-0.55 w0.34
ð aod retro-calculatedfromDNI&GHIwithSolis
FromAeronet network(groundmeasurements)ð level2.0(ifnotavailable,level1.5)indailyvalues
Comparisonð Solisretrofit/aeronet aod (dailyvalues):5%bias,good
correlation(bothissuedfromgroundmeasurements)ð Macc/aeronet(dailyvalues):highdispersionandbiasð w(Ta,HR)/aeronet:nobias,highdispersion,lowimpacton
themodelsresults
Inputdata
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
Overallresultsð negligiblebiasð GHI:sd =2.5%- 3%ð DNI:sd = 3%- 10%
Standarddeviationofthebiasð GHI:∼ 20W/m2 3.5%ð DNI:∼ 25- 39W/m2 3%- 5%ð DIF:∼ 25W/m2 25%
Clearskymodelvalidation
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
Biasdependence
ð with theaerosolopticaldepthð slightdependencewithMACCaerosol
asinputð nospecificpatternwithbmpi aod as
input
ð nospecificseasonaldependence
ð thedistributionofthebiasaroundthe1:1axisisnearofnormal(exceptforDavosandMtKenya)->firstorderstatisticreliable
ð thehighestbeammeasurementsareneverreachedbythemodelledvalueswhateverthemodelis
Clearskymodelvalidation
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
Representationsð DiffusefractionDHI/GHI,Kt andKb
versussolarelevationangleandKt
Clearskymodels’behavior
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
Overallvalidationresults:
ð performanceoverallthedata(110site-year,475’000hourlyvalues,43’000dailyvalues,1’700monthlyvalues)
í hourly: nobias,sd(Gh)=17-20%, bias2%,sd(Bn)=34-50%
í daily: nobias, sd(Gh)=8-12%, bias2%, sd(Bn)=20-32%
í monthly: nobias, sd(Gh)= 3-6%, bias2%, sd(Bn)=9-17%
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 33 800% 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 0.620% 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 9.60% 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 5 621% 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 0.411% 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 5.31% 4% 0% 11% 4% 12% 0% 5% 2% 11% 2% 12% 1% 6% 0% 15% 4% 13%
bias sd
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 IrSOLaVGh 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 42.5Monthly
[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%
3.6% 9.3% 9.6% 3.7% 9.1% 6.3% 4.2% 12.0% 13.8%-80%
-60%
-40%
-20%
0%
20%
40%
60%
80%
Global irradiation Beam irradiation Diffuse irradiation
SolarGis Helioclim3 Solemi Heliomont CM-SAF IrSOLaV
hourly
mon
thly
daily
All-weathermodelvalidation
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
Validationresults
ð Gh:nearnormalbiasdistributionaroundthe1:1axis->reliablestatistics
ð Bn:nonnormalhighdispersion,thestandarddeviation
ð similarbehaviorforallthemodelswiththeclearnessindexKt
ð Model/measurementscomparablemonthlydispersion
Validationresults
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
Validationresults
ð Coherentfrequencydistributionfortheglobal
ð exceptforDavos(snow)
ð Randompatternforthebeamclearnessindexfrequencydistribution
ð 94%oftheglobaland83%ofthebeammonthlyvaluesarewithinonemeasurementsstandarddeviation
Validationresults
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
Validationresults
ð helioclim 3v4:poorclearskymodeltakenintoaccountinthefinalmodel,correctedinhc3v5
ð betterresultswithdailyinputparametersinsteadofmonthlyclimaticdatabanks
ð BettersnowmanagementinHeliomontandSolarGis
Validationresults
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
All-weathermodelvalidation
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
Interannualvariability
Histogramsoftheannualtotal
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
Interannualvariability
Allsitesoverallstatistics:systematicbiascomparedtotheinterannualvariability
ð Gh:allreal-timemodelswithinonemeasurementsstandarddeviation
ð Gh:TMY:2/6modelswithinonemeasurementsstandarddeviation
ð Bn:exceptSolemi,allmodelswithinonemeasurementsstandarddeviation
PVG
IS-CMSAF
WRD
C(198
1-19
93)
RetScreen(1
961-19
90)
NAS
A-SSE(198
3-20
05)
MN
7 (1
980-
2000
)
ESRA(198
1-19
90)
Satellight(1
996-20
00)
SolarGis(20
04-201
1)
Helioclim(2
004-20
11)
Sol
emi (
2004
-201
1)
Heliomon
t(20
06-201
1)
CM
-SA
F (2
004-
2011
)
IrS
OLa
V (2
006-
2011
)
NAS
A-SSE(198
3-20
05)
MN
7 (1
980-
2000
)
Satellight(1
996-20
00)
SolarGis(20
04-201
1)
Helioclim(2
004-20
11)
Solem
i(20
04-201
1)
Heliomon
t(20
06-201
1)
CM-SAF
(200
4-20
11)
IrSO
LaV(200
6-20
11)
SitesAlmeria 1850 2.5% 1.8% -8.1% -8.1% -3.0% 4.9% 0.4% 6.1% 3.0% 3.2% -0.1% 0.3% 2126 5.5% -3.8% -11.1% 15.1% -1.9% 12.1% -3.0% 6.3% 2.0% -3.9%Bratislava 1176 2.9% 3.2% 1.0% 1.1% -1.0% 1.7% 4.3% -3.5% 3.2% -0.2% 5.4% 2.8% 6.5% 4.4% 1191 7.4% -4.0% -7.5% -11.9% -9.6% -2.1% -21.8% -15.3% 3.5% 9.1%Carpentras 1587 2.1% 2.5% -4.8% -15.0% -6.0% -2.8% -5.4% 0.4% 0.3% 0.6% 2.7% 1.6% 2.1% 1.2% 1884 4.1% 0.4% -10.1% 4.9% -1.8% -0.3% -4.2% 3.0% 4.4% -1.6%Davos 1383 1.3% -0.8% -2.7% -7.9% 2.1% -2.9% -17.5% -4.2% 11.5% -13.2% -4.4% -14.5% -7.1% 1420 8.4% -8.0% 18.1% -26.2% -2.8% 21.9% -33.6% -9.5% -27.0% -36.9%Geneva 1282 2.3% 3.5% -6.3% 0.1% 0.1% -4.9% -5.5% -0.6% 4.2% 0.1% 6.4% 3.5% 4.1% 5.3% 1274 3.3% 4.3% -9.8% -0.9% 7.0% 1.9% -4.2% 8.6% 9.4% 4.0%Kassel 1048 2.7% 0.6% -5.6% -5.6% -5.8% -6.6% -5.9% -0.1% -3.4% 0.8% -2.9% -1.4% 4.0% 874 6.4% 1.0% -7.9% -5.1% 2.0% 10.2% -19.4% -4.4% 7.4% 21.8%Lerwick 810 4.7% -4.3% 9.2% 9.1% -3.5% -4.4% -2.5% 0.7% 5.4% 3.8% -3.2% -5.5% 580 13.3% 55.5% 18.2% 0.8% 6.9% 50.4% -11.5% 21.6% 8.5%Lindenberg 1120 3.8% -3.7% -3.8% -3.8% -9.8% -3.9% -12.3% -4.5% -3.1% -2.1% -3.5% -4.8% -0.5% -0.4% 1026 9.6% -8.1% 1.4% -0.4% -6.4% 5.8% -27.9% -15.1% 2.6% 30.5%Madrid 1697 4.9% 3.5% -5.2% -5.2% -3.1% -2.5% 1.7% 1.4% 4.4% 5.7% 5.6% 3.4% 1.9% 1798 5.2% 10.0% -0.8% 14.1% 5.4% 8.6% 4.8% 16.4% 11.0% -2.3%Nantes 1266 3.4% 1.5% -5.2% -3.4% -6.7% -2.2% -0.9% -2.7% -3.3% 0.4% 3.3% 0.7% -0.2% 1.3% 1307 6.7% -12.1% -9.6% -8.8% -8.4% 2.7% -11.1% 4.2% 0.6% 0.7%Payerne 1278 2.4% 1.7% -8.4% -2.5% 0.4% -1.9% -8.3% -2.8% 0.7% -6.4% 1.8% -0.1% -0.3% 4.8% 1191 4.4% 11.1% 5.9% 2.0% 7.0% -3.4% -5.8% 6.1% 7.5% 12.5%SedeBoqer 2114 1.2% -9.2% 0.5% -6.7% -3.9% -4.0% 0.6% -6.1% 3.4% 4.7% 0.9% -1.7% 2382 3.6% 4.6% -5.4% -4.6% -16.9% -8.7% -3.1% -4.8% -7.8%Tamanrasset 2345 1.8% -2.8% 0.8% 2.6% -8.1% 0.9% -1.2% 2.1% -1.8% -1.0% 0.0% 2355 4.0% 6.1% 18.1% 2.5% 14.7% -10.5% -9.2% 6.3%Toravere 981 3.8% 3.1% 3.1% -0.1% 4.6% -2.3% 2.1% -1.5% -4.4% -6.3% -0.8% 1028 8.8% 8.4% 2.4% 7.2% -6.5% 7.2% -28.4% -14.3% -11.7% 1.9%Valentia 1021 4.6% 9.4% -3.9% -4.8% 8.0% -5.3% -4.7% -4.2% -3.6% 4.1% 3.2% 1.8% -5.1% 1.4% 992 13.4% 10.7% -21.5% -21.3% -21.6% 3.3% -25.1% -2.3% -20.1% 1.9%Vaulx-en-Velin 1304 4.4% 3.4% -7.8% -4.0% -3.0% -6.3% -3.3% 0.4% 3.1% 2.6% 7.3% 5.9% 5.0% 3.6% 1359 5.3% -2.1% -11.6% -0.5% -0.9% 4.2% -4.7% 10.1% 7.9% -0.5%Wien 1175 2.7% 0.5% -6.8% -6.0% -0.8% 1.0% -7.0% -1.4% -0.3% -3.0% 3.4% 0.4% 2.7% 0.7% 1112 8.0% 2.9% -3.1% -2.5% -2.3% 4.0% -12.7% -3.5% 11.3% 15.0%Zilani 1024 3.3% -6.1% -3.2% 2.5% -2.6% 6.0% -1.4% 10.9% -3.2% -2.6% -5.9% -17.6% 1000 9.1% 13.4% -0.1% 20.5% -0.2% 31.9% -26.5% -7.3% -5.5% -13.6%
Allsites 1359 2.9% 0.1% -3.5% -3.5% -3.3% -2.3% -4.5% -1.6% -0.1% 1.4% 1.8% 1.0% 0.1% 0.5% 1383 6.3% 3.8% -1.6% -0.1% -1.6% 5.9% -11.3% 0.1% 1.9% -0.4%
3.4% 4.0% 5.1% 5.1% 3.0% 5.1% 3.9% 1.7% 3.9% 3.9% 2.9% 2.7% 3.0% 7.5% 9.3% 9.5% 4.8% 10.0% 12.0% 7.8% 7.5% 7.8%
4.6% 4.6% 6.5% 6.3% 3.4% 5.7% 5.9% 2.1% 5.1% 4.8% 3.6% 3.7% 4.2% 9.0% 11.9% 13.2% 5.9% 13.9% 14.5% 9.3% 9.1% 12.0%
mbd higherthantwostandarddeviationsmbd withinonestandarddeviation
Beamirradiation,meanbiasdifferencembdYearlyto
tal[kW
h/m2]
averageover2
004-20
10
standarddeviatio
nover2
004-20
10
Yearlyto
tal[kW
h/m2]
averageover2
004-20
10
standarddeviatio
nover2
004-20
10
Standarddeviationofmbd
Allsitesabsolutemeanbias
Globalirradiation,meanbiasdifferencembd
mbd withintwostandarddeviations
6th Performance Modeling Workshop 2016Pierre Ineichen, Université de Genève
Publications:http://www.unige.ch/energie/fr/equipe/ineichen/
Thankyouforyourattention