SOLAR RESOURCE DATABASES VS ARITHMETIC MEANS – RESULTS OF A BENCHMARKING EFFORT FOCUSING ON THE NETHERLANDS Matthias Egler e4r – engineers for renewables GmbH, Poststr. 11/12, 10178 Berlin / Germany Phone: +49 30 4372 5105-8, e-mail:
[email protected] INTRODUCTION To establish the solar resource of a given location as accurately as possible, continuous evaluation of available databases and approaches against ground measured reference data is expedient. For locations in the Netherlands, on-site recorded Global Horizontal Irradiation (GHI) is available through an interactive data selection website [1] of the Dutch’s national weather service, Koninklijk Nederlands Meteorologisch Instituut (KNMI). In terms of approaches, market practice is to either use or select a single database or to determine an arithmetic or weighted mean including several different datasets. REFERENCE DATA In this work, hourly GHI time series recorded at 32 automated weather stations (AWS) of KNMI is used. All, but one AWS are equipped with a Kipp & Zonen CM11 pyranometer, i.e. Secondary Standard radiation sensor. The one remaining is simply reported featuring a “pyranometer”. The considered reference period is 2007 to 2016. The reference locations are displayed in Figure 1.
Figure 1: Spatial distribution of the reference locations (green dots) over the Netherlands Data availability has been established reviewing the hourly time series for daytime readings. Months lacking more than 5 % of the daytime data points were rejected. Otherwise, monthly sums were made including an adjustment for reduced data availability, if necessary. Finally, the available monthly data were taken to establish monthly means and resulting annual sum for the reference period from 2007 to 2016. In addition, variability of annual values is established using the resulting complete years. 1
SOLAR RESOURCE DATABASES VS ARITHMETIC MEANS – RESULTS OF A BENCHMARKING EFFORT FOCUSING ON GERMANY Matthias Egler e4r – engineers for renewables GmbH, Poststr. 11/12, 10178 Berlin / Germany Phone: +49 30 4372 5105-8, e-mail:
[email protected] DATABASES AND ARITHMETIC MEANS The databases included in the benchmarking are PVGIS-CMSAF [2], PVGIS-SARAH [2], CAMS-RAD [3] and NASA POWER 1/2° x 1/2° [4]. The periods covered vary, but in each case data matching the reference period is available. In case of PVGIS-CMSAF, PVGISSARAH and CAMS-RAD time series of hourly values have been retrieved. NASA POWER 1/2° x 1/2° is available in daily resolution only. Four different arithmetic means were considered, titled as follows and with the following databases included: • Mean – PVGIS (PVGIS-CMSAF and PVGIS-SARAH) • Mean – HighRe (PVGIS-CMSAF, PVGIS-SARAH and CAMS-RAD) • Mean – All (PVGIS-CMSAF, PVGIS-SARAH, CAMS-RAD and NASA POWER 1/2° x 1/2°) • Mean – PVGIS-NASA (PVGIS-CMSAF, PVGIS-SARAH and NASA POWER 1/2° x 1/2°) APPROACH AND RESULTS Root Mean Square Error (RMSE) is considered main indicator and metric with regard to general performance and accuracy. Overall relative RMSE has been determined using the bias between the annual long-term values or GHI variability provided by the databases or arithmetic mean values against the corresponding reference value at each location. In addition, global minimum and maximum deviation, Mean Bias Error (MBE) and Mean Absolute Error (MAE) were calculated and taken into consideration. Of note, to ensure liketo-like comparison, the long-term values of each database and arithmetic mean are matching the available months in the reference dataset. Figure 2 presents results regarding long-term annual GHI values.
Figure 2: Global absolute maximum deviation (dashed lines) and relative RMSE (bars) for each single database and various arithmetic means with regard to long-term annual values. All arithmetic mean values feature smaller relative RMSE than three out of four different, individual databases. Same is true with regard to absolute maximum deviation. In this GHI benchmarking, PVGIS-SARAH database is considered best performer. In relation to monthly long-term data, very similar pattern can be observed, however with slightly elevated numbers due to the smaller temporal granularity. The main performance 2
SOLAR RESOURCE DATABASES VS ARITHMETIC MEANS – RESULTS OF A BENCHMARKING EFFORT FOCUSING ON GERMANY Matthias Egler e4r – engineers for renewables GmbH, Poststr. 11/12, 10178 Berlin / Germany Phone: +49 30 4372 5105-8, e-mail:
[email protected] indicator in this case is the overall average of each location’s RMSE determined using the monthly biases. Finally, with regard to inter-annual GHI variability the performance has been assessed using RMSE, overall negative and positive deviation as well as regression coefficient R². Figure 3 displays the findings in relation to annual GHI variations.
Figure 3: Relative RMSE as well as negative and positive deviation of each single database and arithmetic mean with regard to annual GHI variability. Again, the accuracy of the arithmetic mean values is relatively constant, but this time all combinations outperform each individual database. The most sensible approach seems to combine PVGIS-CMSAF, PVGIS-SARAH, CAMS-RAD and NASA POWER 1/2° x 1/2° datasets, as this provides the smallest relative RMSE and the most uniform deviations. However, “Mean – HighRe” arithmetic mean performs similarly and thus is a fair option as well. [1] [2] [3] [4]
Climatology –historic weather information: http://projects.knmi.nl/klimatologie/index.html European Commission Joint Research Institute, Photovoltaic Geographical Information System (PVGIS): http://re.jrc.ec.europa.eu/pvg_tools/en/tools.html Copernicus Atmosphere Monitoring Service (CAMS) radiation (RAD) service: http://www.soda-pro.com/en_GB/web-services/radiation/cams-radiation-service NASA Prediction of Worldwide Energy Resource (POWER), Higher Resolution Daily Time Series by Location: https://power.larc.nasa.gov/cgi-bin/hirestimeser.cgi
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