Scaling and Evaluation of Wind Data and Wind Farm Energy Yields 1/9

Scaling and Evaluation of Wind Data andWind Farm Energy YieldsEnglish Version from Article in DEWI Magazin No. 23

W. Winkler, M. Strack, A. Westerhellweg, DEWI Wilhelmshaven

1. SummaryWind data and energy yields are converted (scaled) to long-term periods by suitable methods. Long-termdata suitable for the correlation of wind measurement data are data of meteorological stations andReanalysis wind data. Energy yields of wind turbines already installed are usually scaled in Germany bymeans of so-called wind indices in order to calculate the long-term average energy yield. The followingarticle mainly deals with the selection of suitable long-term data and is an update and extension of [1].The article concentrates on the wind indices used most frequently in Germany. With regard to the appli-cability of wind indices there are significant parallels to the global usability of long-term wind data as faras the correction and selection of the data are concerned.

2. Wind indices for long-term correctionWind speeds and therefore also the energyyields of wind turbines are subject to temporalfluctuations. If energy yield data are available fora limited period only, they generally differ dis-tinctly from the long-term average. For annualenergy yields, these deviations from the long-term average may well be 20 % and more inextreme cases. Monthly values usually arescattered far more.

A wind index (or energy yield index) scales theseenergy yields with reference to a pre-definedlong-term mean value and thus allows to calcu-late the long-term mean value of the energyyield.

The scaled energy yields obtained in this wayare used, among other things, to carry out plau-sibility checks on the meteorological data input ofenergy yield assessments. This is due to the factthat in Germany energy yield predictions nor-mally are carried out on the basis of the Euro-pean Wind Atlas Procedure [2] and using data ofremote meteorological measuring stations. Themeteorological data used in this process includea considerable amount of uncertainty, whichmakes it necessary to perform a plausibilitycheck by comparing the data with actual energyyields of the same region.

Energy yields scaled by means of wind indicesare also used for the monitoring of existing windturbines or wind farms. This allows to examinehow the wind turbines are behaving in compari-son with the regional wind conditions, and so todetect any reductions in energy yield due to windturbine failures. The scaling of energy yields also

makes it possible to compare the energy yieldpredicted before commissioning of the wind tur-bine/farm with the energy yield actually achieved(see below). Wind indices are furthermore usedfor the extrapolation of wind data measuredmonthly or yearly to long-term periods.

In Germany, the wind indices issued by Inge-nieurwerkstatt für Energietechnik (IWET) [3] aregenerally considered to be standard values andare therefore treated with preference here. Apartfrom the IWET indices (also known as Keiler- /Häuser index), the Internationales Wirtschaftsfo-rum Regenerative Energien (IWR) [4] also issueswind indices which are often quoted in the lit-erature. The IWET indices are characterised asfollows:

� There are 25 regional Indices.� The index values are determined from se-

lected monthly mean values of wind turbineenergy yields of a region.

� The 100 % level was determined on the basisof a comparison with neighbouring wind indi-ces (originally by comparison with the Danishwind index of the eighties) and in most re-gions corresponds to approx. 100 % for theperiod between 1989 and 1999.

� IWET as well as IWR indices refer to themonthly mean values of energy yields, andtherefore do not allow a detailed analysis oftime series of wind speed and wind direction.In this respect, wind indices simplify the pro-cedure.

Scaling and Evaluation of Wind Data and Wind Farm Energy Yields 2/9

3. Wind measuring data for long-term correc-tion

Outside Germany and Denmark, correlationswith other long-term wind measuring data (e.g.from meteorological stations) are carried outnormally in order to extrapolate wind measuringdata to longer periods of time. Provided the on-site measurements have been carried out forone or more years and are to be converted to along period (10-30 years) using wind data ofmeteorological stations, it is often safe to as-sume that the wind distribution is already re-flected by the measuring data. Because of thefluctuations of the annual wind conditions, it isnevertheless necessary to scale the measuredwind data to a long period. In view of the resolu-tion and the characteristics of available long-termwind data, a detailed correlation often has to beruled out. If the measuring data obtained at thesite were taken only during a few months (oreven only during a few weeks) and have to beconverted to long periods for the purpose ofenergy yield prediction, high-quality correlationprocedures are required. The use of high-quality"Measure-Correlate-Predict" (MCP) proceduresis particularly important in complex terrain andfor the evaluation of measuring data obtained atdifferent heights (e.g. SODAR data) or at differ-ent atmospheric conditions.

The successful use of these procedures requiresexperience and access to suitable referencedata. In an MCP procedure the time series ofwind speed and wind direction are analysed indetail and mapped onto each other. DEWI hasdeveloped procedures by means of which soundresults can be obtained even from short timeseries, provided that reliable reference data areavailable ([5], [6]).

With regard to the correct realisation of long-termcorrections, the use of suitable procedures aloneis not sufficient: another important aspect in thelong-term correlation of wind data is the selectionof suitable long-term data and the checking andsometimes correction of them. From our long-term and ongoing experience with energy yieldpredictions and the correlation with meteorologi-cal long-term data we know that for various rea-sons, and often much more than generallyassumed, meteorological long-term data canlead to false estimations of the wind potential,due to errors and inconsistencies in these data.

Meteorological data almost never meet the re-quirements as to accuracy that have to be ful-filled by wind measurements for energy yieldpredictions. Nevertheless they can be used for

long-term correction, if after having been filtered,analysed and compared with other data, they areconsidered to be sufficiently consistent and con-nected with the wind measurements in question.

In order to prove the connection between variouswind measurements, it is not always helpful tosimply observe the correlation coefficient (R orR²) because of the characteristic features of winddata time series. This, too, requires a detailedanalysis. DEWI has access to and experiencewith a large number of data sources and hasdeveloped the tools necessary for the analysisand processing of these data. Fig. 1 shows long-term data available on the example of Spain.Quite often it is safe to assume that the nearestmeteorological station is not necessarily the bestsuited station for the long-term correction of winddata.

Fig. 1: Map of long-term measuring stations inspain

4. Long-term correction using wind indicesThe averaging and selection of the data inputused for IWET wind indices are an importantadvantage of these indices. However, as withmeteorological long-term data, when using en-ergy yields and wind indices, it is necessary tocheck if they are consistent and if there is a clearconnection between them. Fig. 2 shows by wayof example the interrelation between energyyields and wind indices.

Scaling and Evaluation of Wind Data and Wind Farm Energy Yields 3/9

R2 = 0.88

0

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0 50 100 150 200 250

relative energy yield [%]

win

d in

dex

[%]

extreme values

wrong values

Fig. 2: Energy yield of a wind turbine and wind index values

The energy yields in this example and in thefollowing were taken from the Betreiber-datenbank (operators' data base) [7], publishedby IWET. When taking a closer look at this ex-ample, the following is noticed:

� Energy yields and wind indices are clearlyconnected with each other, as shown by thelinear trend line depicted.

� The values of the wind index fluctuate morestrongly than the energy yield, thus resultingin an overcorrection.

� Not all of the energy yield data can bemapped at first sight onto the wind index, es-pecially where wrong data or extreme valuesare concerned. Here a detailed analysis willbe necessary.

The overcorrection mentioned is explained inFig. 3. This figure shows the time curve of themonthly energy yield and the respective regionalwind index as well as the resulting scaled energyyield for an exemplary wind turbine. The scaledenergy yields of this exemplary wind turbinefluctuate considerably around the mean value;the wind index here leads to a distinct overcor-rection of the energy yield. For example, theactual energy yield in February 2002, which wasa strong-wind month, was far above the average.Since the corresponding value of the IWET indexis even higher for this month, the scaled energyyield consequently would be clearly below theaverage. The exact opposite can be observed inthe summer of 2001 with low-wind.

The tendency of the IWET index towards over-correction occurs in many cases and meanwhileis generally known in the trade.

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Dez 00 Mrz 01 Jun 01 Sep 01 Dez 01 Mrz 02

ener

gy y

ield

[MW

h] win

d in

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energy yieldstandardised energy yieldstand. energy yield with modified indexwind index

100 %

Fig. 3: Example of energy yields standardized by the IWET wind index and an improved standardisation by a modified IWET index

The overcorrection may be due to the fact thatthe IWET index orients itself to the energy fluxdensity (W/m²) and not to the energy yield itself.Occurrence and extent of the overcorrectiondepend on the region, but also on the site, thetype of wind turbine and the hub height. Roughlyspeaking, there seems to be a connection be-tween the overcorrection and the full-load hours.

When analysing the data, the extent of the over-correction can be determined and the wind indexcan be modified accordingly and adapted to thespecific site. The monthly energy yields scaledon the basis of a modified wind index are alsoshown in Fig. 3. The modified site-specific windindex results in an improved scaling. This scalingis not complete, however, which means thatsome degree of uncertainty remains, even if thewind index has been modified.

The uncertainty of the scaling is shown in Fig. 4for the same energy yield data as in Fig. 3. Itwas calculated from energy yield data availablefor a total period of six years for different lengthsof time (1, 2, ..., 24 months). As expected, theuncertainty decreases with the length of the pe-riod considered, roughly with the root of thenumber of the data records. It should be notedthat without an individual adjustment of the IWETindex the uncertainty of scaling would begreater. In particular there are seasonal influ-ences with regard to the connection betweenwind index and energy yield of individual windturbines. When energy yield data are not avail-able for a complete year, it is especially impor-tant to adjust the wind index individually. Theseasonal influences disappear when using indi-vidually adjusted indices.

Scaling and Evaluation of Wind Data and Wind Farm Energy Yields 4/9

90%

95%

100%

105%

110%

0 5 10 15 20 25number of months

stan

dard

isat

ion

leve

l

standard uncertainty (68%-confidence intervall)

95% confidence intervall

variation/square root (months)

Fig. 4: Uncertainty of the modified standardi-sation as a function of the period for which energy yield data are available

It should also be noted that of course there maybe values exceeding the standard deviation. Thedeviations reach their maximum at extreme windindex values. This is true for example for theperiod depicted in Fig. 3.

5. Long-term correction using long-termwind data

A long-term correction is often performed on thebasis of the mean values of long and short peri-ods, i.e. a linear connection (without offset) be-tween the data is assumed. Where this pre-requisite is not fulfilled, more complex proce-dures (such as MCP) should be applied. Itshould also be mentioned that correlations maydiffer considerably depending on the season [5]).

Fig. 5 shows by way of example the relativedistribution of wind speeds of a site measure-ment in comparison with meteorological stations.In this case the time curves of the data of bothlong-term measuring stations are plausible. Thelong-term correlation with station 2, however,resulted in a long-term mean value of windspeeds approx. 4% higher than station 1. Thecorrelation of the monthly mean values for bothstations with an R² of approx. 90% is very high.

However, at station 2, when looking at a timeseries with higher resolution for December 2002and the following months, it turns out that due tomissing data the mean wind speeds are too low,which largely explains the difference. Station 2 isvery close to the site examined. Without thecomparison with another station, the data mighthave been used in spite of the missing data.

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Jul 00 Jan 01 Jul 01 Jan 02 Jul 02 Jan 03

rela

tive

win

d sp

eed

site wind meas.

met. station 1

met. Station 2

Fig. 5: Realtive wind speeds of a site measurement in comparison with meteorological stations

This example shows the necessity of a detailedcomparative analysis and evaluation of any dataused for long-term correction, because otherwisethere may be drastic divergences from a realisticenergy yield level. In our example, although atwo-year wind measurement with good resultswas carried out on the site, the difference wouldbe approx. 6% of the energy yield.

If a wind measurement is located between twometeorological stations, it can be explained andsometimes actually observed that this windmeasurement correlates best with data averagedfrom the two stations. Averaging of long-termdata also has the advantage that any inconsis-tencies yet unrecognised have a less severeeffect in the long term; but this does not apply inall cases.

6. Definition of the long-term 100 % levelWhile it is comparatively easy to establish a sta-tistical connection between two measurements,the determination of wind conditions actually trueon a long-term average is much more difficult.Meteorologists generally base their long-termaverage data on climate time series of 30 years.At least as far as wind measurements are con-cerned, we know from experience that there arealmost no complete consistent 30 years' timeseries. When in doubt, shorter periods are bettersuited if there are inconsistencies in the long-term data.

In order to find out if the 100 % level of the IWETwind indices selected is true, the following inves-tigations were made:

� comparison with a "geostrophic" wind index,� comparison of wind index values in North

West Germany with long-term time series of

Scaling and Evaluation of Wind Data and Wind Farm Energy Yields 5/9

the wind conditions of the Deutscher Wetter-dienst, and

� comparison of several indices with eachother.

6.1 Preparing a "geostrophic" wind indexThe wind data described here as "geostrophic"were taken from the reanalysis data base ofNCEP/NCAR [8]. In short, these are the resultsof a global climate computation model whichuses a large number of filtered and converteddata. The reanalysis data comprise various pa-rameters and are available world-wide with a griddivision of 2.5° latitude/longitude and also fordifferent altitudes (given as pressure levels).

DEWI uses wind data from high heights, asthese are normally free of local influences andcan therefore be considered as representativefor larger areas. Because of the high altitude,these data are described here as "geostrophic"wind data. As expected and as already knownfrom experience, these "geostrophic" wind dataare very consistent in many regions of the earthand are therefore - not only in Germany - wellsuited for a long-term comparison. This state-ment is not true, if, for example, local, e.g. ther-mal, effects have a significant influence on windconditions near the ground.

Fig. 6: Grid points for the geostrophic wind index in Germany

The period beginning 01/1982 was used as adata basis, and the long-term average of thisperiod was established as the 100% level. Datawith a 12-hourly resolution (1h and 13h, MET),converted to monthly mean values, were used.Germany is covered by 17 grid points. (Fig. 6). Inorder to map the monthly means of the windspeeds onto the energy yields of wind turbines,suitable relations are established, which allowsto establish a realistic connection between windspeeds and wind turbine power outputs. Fig. 7shows the relative course of the energy yield of awind turbine in Rhineland-Palatinate and of thecomputed geostrophic wind index.

A good correlation of the values can be ob-served. Generally, however, the correlation ofgeostrophic wind indices with the energy yielddata of wind turbines is not so good as with theIWET wind indices, which is why geostrophicwind indices are used by DEWI only additionallyand not as a substitute for IWET indices.

In spite of the poorer correlation, geostrophicwind indices supply additional information, be-cause they allow to confirm the long-term windpotential by an additional data source independ-ent of the characteristic features of the windturbine energy yield data. The correlation canfurther be improved by adjusting the geostrophicindices to the respective wind turbine sites. Thusit is possible to determine wind farm-specificenergy yield indices for many places world-wide.

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Jan 97 Jan 98 Jan 99 Jan 00 Jan 01 Jan 02

rela

tive

valu

es [%

]

energy yield

geostrophic wind

Fig. 7: Sequence of a geostrophic wind index incomparison with the energy yield of a wind turbine in the same region

In contrast, the wind indices issued by IWR [4]were computed on the basis of a purely linearrelation between 30 years' geostrophic wind dataand one wind turbine each. Since there are onlytwo regional wind indices for Germany, they donot correlate well with wind turbine energy yieldsand can therefore only give a rough estimate asto the long-term wind turbine energy yield.

Scaling and Evaluation of Wind Data and Wind Farm Energy Yields 6/9

Because of the relatively long-term and pre-sumably consistent data base, DEWI's geostro-phic wind index is particularly interesting withregard to the 100 % level. A summary evaluationproduces the following results:

� The long-term level of the indices of the nine-ties is 102 % for the geostrophic data and100 % for the IWET indices.

� Assuming that the geostrophic indices aresummarily correct, the IWET indices wouldlead to a slight overestimation of the wind re-source.

� The 10-year average values of the 25 IWETindices scatter in the range of 97 % – 105 %,whereas the geostrophic wind indices inGermany only scatter in the range of ± 1%.

� A comparison shows that the wind conditionsin the period 1990-1994 were better than1995-1999. The difference between the indexvalues of the first half of the nineties com-pared with the second half was 0 % – 10 %for the geostrophic wind data (average: 7 %);according to IWET wind indices 6 % - 33 %(average: 15 %).

These differences between the various datasources are partly due to the overcorrection ofthe IWET indices described above, i.e. it doesnot follow necessarily that the long-term mean ofthe index is not correct.

6.2 Comparison of IWET indices with mete-orological data in the area of the GermanBight

Within the framework of the R&D project9946101 of the Federal Ministry of the Environ-ment, long-term data of the DWD (GermanWeather Service) in the area of the GermanBight, partly going back over 60 years, wereevaluated for research purposes [9]. The mainobject of the investigation was to find out in howfar the IWET wind indices were suitable for along-term forecast of the offshore wind re-sources. Fig. 8 shows the location of the mete-orological stations.

The time series of the data partly go back as faras the thirties and are therefore potentially wellsuited to supply information on the long-termbehaviour of the wind resource. As expected,however, there are considerable gaps in thedata, changes in the observation frequency,inconsistencies and unplausible values. A com-plete documentation on the measuring equip-ment used as well as on any replacements andchanges in the direct environment of the meas-uring stations was not available.

Fig. 8: Evaluation of meteorological data from the area of the German Bight

In order to get useful results from these databases, data records or periods presumed to beinconsistent or faulty or from which a lot of datawere missing, were filtered out. After filtering,only the data records of five stations were con-sidered fit for use; they included the nineties. Toallow a comparison between meteorological dataand energy yields, the wind speeds were againmapped onto the energy yields by suitablemethods. According to this evaluation, the aver-age level of the indices in the nineties is

� 100 % – 102 % for the meteorological dataconsidered to be consistent,

� 101 % – 102 % for the geostrophic data of theNorth Sea coastal area and

� 100 % for the IWET indices. This shows thatthere is again a slight general overestimationof the wind resource of this region by theIWET indices.

� For this region, too, it is true that wind condi-tions were better in the first half of the ninetiesthan in the second half. In index values thismeans a difference of 5 % – 10 % accordingto the meteorological data, 7 % according tothe geostrophic wind data, and 9 % – 19 %(average 14 %) according to the IWET windindices.

In other investigations various data from theperiod between 1989-2002 were evaluated andcompared with the IWET index of the coastalregion of Friesland / Ostfriesland. Fig. 9 showsthe corresponding original wind index, an indexadjusted individually and scaled to the period1989-2002, as well as the data of a meteorologi-cal station, a geostrophic grid point and the dataof a measurement taken on the DEWI test site at

Scaling and Evaluation of Wind Data and Wind Farm Energy Yields 7/9

Wilhelmshaven (these were partly correlatedwith another high-quality wind measurement).Other data were also assessed, but are not de-picted here.

1989 1993 1997 2001

inde

x va

lue

wind index, unscaledwind index, scaledgeostropic datamet. stationWHV 62 m

100

Fig. 9: Relative courses of several indices in comparison with the IWET-Index of Friesland / Ostfriesland

The following conclusions can be drawn:

� The values given by the original wind indexfor the last few low-wind years are clearly toolow, due to the overcorrection; for the ex-treme low-wind year 2001 a difference of 6 %– 7 % can be assumed. For wind turbines in-stalled in the last few years, the exclusive useof the IWET index would lead to a noticeableoverestimation of the long-term wind re-source.

� The energy yields of different wind turbinesscaled with long-term wind data scatter onlyaround 1 % irrespective of the scaling indexused (except for the non-adjusted IWET windindex)

� For the complete period 1989-2002 the un-corrected IWET index is approx. 2 % lowerthan all other data observed.

� The mean value of the wind potential of theperiod assessed 1989-2002 corresponds tothe mean value of longer periods (1982-2002,and for one time series 1970-2002) for thelong-term data available.

The average wind resource in the period 1989-2002 therefore can be considered as a long-termaverage. The IWET wind index values for thisregion are 2 % below the correct values, i.e. thewind potential would be overestimated by 2 %.

6.3 Comparison of different wind indicesFor the wind index region discussed above acomparatively large amount of energy yield datais available; this is not the case in other regions.The problems arising from this are demonstratedby the data given in Fig. 10. The data shown arethe annual average values of the wind indices

scaled to 1989-2002 of two neighbouring windindex regions in the area of South Brandenburg,Thuringia and Saxony, each adjusted to thesame wind turbines.

1989 1991 1993 1995 1997 1999 2001

Inde

x va

lue

wind index 17, scaled

wind index 20, scaled

100

Fig. 10: Comparison of wind index values of two neighbouring regions

The investigation has shown that:

� For a few years now the relative wind condi-tions of region 17 would have been up to 10% poorer than in region 20.

� The good correlation of the data would makeit seem likely that a correction of the wind tur-bine data to the long-term average would bepossible with both indexes. For the wind tur-bines examined (with energy yield data ofapprox. 3 years) the difference between theindices, however, would lead to a differencein the energy yields of approx. 5 %. Whenlooking at individual years, the differencewould be bigger.

� In region 17 the first wind turbines contribut-ing to the index have been installed since1994; in region 20 since 1992. Earlier indexvalues were produced by comparison withother indices.

� The index values produced by comparisonwith other regions are uncertain. Using themobviously leads to artefacts, as can be seenin the case discussed here. The noticeabledivergence of the index of region 17 in theyear 1993 (when there were no wind turbinesinstalled in the index region 17) from the val-ues of region 20, for example, is not plausiblefor the assessments discussed here.

� In many other cases an individual adjustmentof the indices to wind turbine data will reducethe differences between the energy yieldsscaled by means of two different wind indi-ces.By contrast, the difference in the presentcase would be even greater, which is obvi-ously caused by the index values before

Scaling and Evaluation of Wind Data and Wind Farm Energy Yields 8/9

1994. When scaled to the period 1995-2002(8 years) in which sufficient wind turbines areinstalled in both regions, the difference of thescaled energy yields is reduced to a realistic2 %.

Generally speaking, the longest possible periodsof index values (e.g.1989-2002) therefore do notalways supply the most suitable data for scaling.As with the meteorological long-term data, it isnecessary to use a period considered to be con-sistent for the long-term correction. DEWI alwaysscales the results of energy yield predictions onthe basis of the longest period of reference dataconsidered to be consistent in a detailed analy-sis. For this analysis the reference data them-selves are used, as well as results from otherdata sources available. In the case of wind indi-ces, this regularly leads to a re-scaling of windindex values.

It should also be noted that the IWET wind indi-ces are automatically inconsistent, because newwind turbines, normally bigger, higher and moreefficient, are continuously being included in thecomputation of the indices. With regard to theso-called "overcorrection", they have a differentbehaviour than wind turbines installed earlier.There is also the tendency to install more andmore wind turbines at sites with poorer windresources. In the vicinity of already existing windturbines, very often additional wind turbines areinstalled, which reduce the wind farm efficiencyof the turbines erected first and thus also influ-ence the overall values of the wind indices de-rived from them. The question whether thesefactors have a really relevant effect can only beanswered by comparison with data independentof the wind index.

7. Uncertainties of long-term correctionNo matter how carefully you are working, thelong-term correction of wind and energy yielddata can never be perfect. The uncertainty oflong-term correction is composed of the followingcomponents:

1. the statistical uncertainty of correlation, ex-pressed by incomplete mapping of the data

2. the uncertainty of the correction or correlationprocedure (possibly, this may coincide with(1)).

3. the uncertainty whether the long-term periodof wind or energy yield data considered isfree of inconsistencies and errors

4. the variation of the long-term average of sev-eral years as opposed to a very long (e.g. 30

years' average.5. additionally and irrespective of (4) the uncer-

tainty whether the future wind resource (e.g.for the next ten or twenty years) correspondsto the period examined in the past.

Items (1), (4) and (5) can be determined from thelong-term data examined, or, where these arenot available, from experience values. Annualwind data, for example, often scatter in a rangeof 5 % to 7 % of the wind speed; this applies tomany data evaluated world-wide. In case ofstrong local influences on the wind resource, thedata may also scatter differently. The degree ofuncertainty is reduced with increasing duration ofthe periods considered.

With regard to items (2) and (3) there are hardlyany analysis methods that are generally applica-ble; however, they should not be neglected alto-gether and at least be estimated. A realisticanalysis of the uncertainties of long-term correc-tion is the pre-requisite for reducing it by suitablemeans (e.g. by examining other long-term dataor by extensive correlation procedures). Theanalyses described above, which are carried outby DEWI regularly, also reduce the risk of usingnon-consisting data for the long-term correction.

8. Conclusions and outlookWhen using wind indices and wind turbine en-ergy yield data as well as meteorological long-term data, a careful quality check, selection andadaptation of the data are necessary in order toavoid misinterpretations. One should be aware,however, that each correlation of long-term dataincludes a certain degree of uncertainty that isinherent in the system. It is necessary to makesure that there is a sufficient connection betweenthe long-term data and the site-specific data tobe assessed, and that a plausibility check iscarried out for the consistency of the long-termdata.

In our everyday work we find the operators' database and the wind indices to be extremely im-portant and helpful for making energy yieldspredictions. This is particularly obvious in com-parison with other countries where only mete-orological data are available as reference data.In any case the indices and operating data usedshould be examined in detail and the informationsupplied by IWET on the indices should be takeninto account. The scaling by means of IWETindices can often be improved by individualmodification of the indices. When using theseindices, they should be scaled to a period that is

Scaling and Evaluation of Wind Data and Wind Farm Energy Yields 9/9

as long as possible, but consistent; if necessary,other data should be included in the analysis.

Any uncritical and superficial application of me-teorological data of IWET wind indices may eas-ily lead to a false estimation of the long-termenergy yield in the double-digit percent range.Because of the general use of wind indices inGermany, possible faults can have particularlyserious consequences: when the prediction ofthe energy yield as well as the verification of theoperating results are based on the same windindex, possible faults in the scaling of the windindex may remain concealed for years, while thewind farm operator is waiting for the proverbialseven fat years. Faults are only discovered whenit is too late.

Uncertainties with regard to wind indices can bereduced in particular when independent datasources and analysis methods (MCP) are used.For example, indices can be determined from"geostrophic" wind data or from the energy yieldsof neighbouring wind turbines, or long-term windmeasurements can be used.

The assessment of the long-term energy yield tobe expected of a wind farm and the computationof reliable wind farm-specific energy yield indicesis important and can be realised within a certainrange of tolerance by using suitable data and thecorresponding know-how. On this basis pros-pects are developing which will allow to accountfor the fluctuations of the wind. The use of highresolution data as monthly mean values willmake it possible to examine individual circum-stances more closely and also to increase accu-racy. With regard to the energy yield of windfarms this is especially important when trying tofind out the reasons behind a decline in the windfarm energy yield.

9. References[1] Wolfgang Winkler; Martin Strack; Annette

Westerhellweg: Zuverlässige Methoden zurNormierung und Bewertung von Energieer-trägen von Windparks, Tagungsband derDEWEK, Wilhelmshaven, 2002.

[2] I. Troen, E.L. Petersen, European WindAtlas, Risø National Laboratory, Denmark,1989.

[3] Häuser, Keiler: Windindices für Deutsch-land, Ingenieur-Werkstatt Energietechnik,Rade.

[4] Internationales Wirtschaftsforum Regen-erative Energien (IWR); IWR-Windertragsindex Küstengebiet, West-

deutsches Binnenland, Münster.[5] V. Riedel, M. Strack, H.P. Waldl: Robust

Approximation of functional Relationshipsbetween Meteorological Data: AlternativeMeasure-Correlate-Predict Algorithms. Pro-ceedings EWEC 2001, Copenhagen.

[6] H. Mellinghoff, M. Strack: Betriebserfahrun-gen und Datenauswertung bei Sodar Mes-sungen, Tagungsband der DEWEK,Wilhelmshaven, 2002.

[7] Häuser; Keiler: WEA-Betreiberdatenbasis,elektronisch vom Herausgeber. Veröf-fentlicht in: Monatsinfo, Keiler-Häuser, In-genieur-Werkstatt Energietechnik, 24594Rade.

[8] E. Kalnay, et. al.: The NCEP/NCAR 40-Year Reanalysis Project, NCAR/NCEP, zu-gänglich unter wesley.wwb.noaa.gov/reanalysis.html.

[9] DEWI; Weiterer Ausbau der Windener-gienutzung im Hinblick auf den Klimaschutz-Teil 2; BMU-F&E Vorhaben 9946101,Wilhelmshaven, 2002.