INCREASING QUALITY AND TIME RESOLUTION OF SATELLITE-DERIVED SOLAR IRRADIANCE TIME-SERIES USING APPROPRIATE SHORT TERM GROUND STATION DATA.

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1 INCREASING QUALITY AND TIME RESOLUTION OF SATELLITE-DERIVED SOLAR IRRADIANCE TIME-SERIES USING APPROPRIATE SHORT TERM GROUND STATION DATA. Hans Georg Beyer Institute of Engineering University of Agder Post Box Grimstad Norway Kaushal Chhatbar Suntrace GmbH Brandstwiete Hamburg, Germany Richard Meyer Suntrace GmbH Brandstwiete Hamburg, Germany ABSTRACT For site assessment of larger solar power projects satellite derived solar irradiance information is the method of choice for setting up multiannual data bases. For detailed system studies, limitations of these bases shave to be addressed. On one hand, satellite derived data sets may differ from high quality ground measurements and have to be appropriately corrected. On the other hand, the time resolution of the satellite derived data is limited to typically one hour (at best 15 min). This resolution is not sufficient when analyzing systems as e.g. concentrating solar power systems (CSP), showing a distinctly nonlinear response and inertia in the system reaction. In this paper, schemes dealing with this two shortcomings are presented, making use of shorter term parallel ground based measurements. An example for the set up of an improved set is discussed for a site in the Sahara desert, where a station of the Baseline Surface Radiation Network (BSRN). 1. INTRODUCTION The inter-annual variability of solar irradiation can be very high depending on location of the sites and hence stable and reliable long-term averages shall not be based on measurement from a few years only. Today satellitederived time-series covering more than 1 years can be made available for every site worldwide. Such longterm time-series help reducing uncertainty of the longterm best estimate. However, differences between satellite-derived and ground-measured data sets can be high indicating a remarkable uncertainty associated with these techniques. Despite having more than a decade of satellite data, uncertainty of the long-term average often is in the range of 5 % to 15 %, especially for direct normal irradiance DNI. However, limited periods of ground-based solar radiation measurements available for the site of interest are expected to be a mean for reducing these uncertainties remarkably. A limitation for the applicability of the satellite derived sets is, however given by their time resolution, which is typically hourly, or at best 15 minutes. As, for example due to their complex response characteristics, concentrating solar power (CSP) systems show non linear response to (direct) irradiance, and a dynamic response governed by various thermal inertias, a reliable modeling the systems has to be based on information on irradiance with time resolutions on a minute-by-minute time scale. Thus, the input data for site specific simulation of system performance have to be based on hybrid sets with satellite information being enhanced by superimposed short term fluctuations. The basic validity of this approach had been demonstrated e.g. by [1]. Means to tackle with these two tasks reducing uncertainty of satellite derived data sets by making use of short term on site data sets and superimposing realistic short term fluctuations to these sets - have

2 recently been proposed. In this paper we are showing the respective data processing chain for the example of data for a site in the Sahara-Region, where data from a BSRN station can be used for validation of the procedure. 2. A SCHEME TO USE INFORMATION FROM PARALLEL ON SITE MEASUREMENTS FOR IMPROVING SATELLITE DERIVED DATA SET OF OTHER TIME PERIODS training test test test Schemes for the derivation of irradiance data from satellite information are mostly in a mature status today, Given that correct information on the status of the clear atmosphere is available and that small scale local effects (e.g. terrain shading) can be neglected, the accuracy of the satellite derived data is mostly satisfying. However, due to the fact that the information on the atmospheric composition may - depending on location be sparse and local scale influences cannot generally be excluded, the satellite derived data have to be controlled and to be corrected, if necessary. Thus, ground based data are still valuable to achieve high quality log term data sets. Given the case, that the ground based measuring campaign has resulted in the identification of significant deviations of satellite and measured data a schemes for a respective correction of the satellite data has to be applied. These schemes try to learn the structure of the error pattern from the periods with parallel measurements and satellite data and use this to correct the satellite data for the satellite only periods. However, this requires, that the causes of the deviations of the satellite data are common for all data under consideration. A scheme for this type of correction applied to direct normal irradiance (DNI) is described e.g. by [2]. It is based on the correction of the cumulative distribution function (CDF) of hourly DNI values in order to approach the one of the measured data. A sketch of this scheme is given in figure1. Fig 1: Top left: cumulative frequency distribution for training time of overlapping ground and satellite time series, the arrow illustrates the difference between both curves. Top right: corrected satellite cumulative frequency distribution for test(application) period; Bottom left: mapping of original satellite irradiance values to original cumulative frequency distribution; arrow and bottom right image: first, mapping of original cumulative frequency distribution to corrected cumulative frequency distribution, second, mapping of corrected cumulative frequency values to adjusted satellite values. Green: ground data from trainings set, magenta: satellite data from trainings set, purple: satellite data from test set, yellow: corrected satellite data. In [2] and [5] the applicability of this procedure for sites in Spain and Israel has be successfully tested. 3. ENHANCING THE TIME RESOLUTION OF DNI DATA SETS BASED GROUND BASED MINUTE- BY-MINUTE DATA SETS. 3.1 Statistical characteristics of DNI series with time resolution of one minute For the statistical characterization of time series of DNI data, the separation of systematic and stochastic components of the fluctuations is commonly done by normalization of the DNI values with the values expected under clear sky conditions DNI_clear, giving the clear sky indices k*dir. In [4], a multilayered scheme for modeling the probability density of these sets is given. For the characterization of the time series properties of the data, the autocorrelation coefficient is used solely. Combining the information on probability density and autocorrelation characteristics a procedure to synthesize data sets is given. Concerning minute-byminute data of global irradiance and illuminance data, respective tools had been developed and applied [see e.g. 3].

3 For the test of the applicability of this scheme in view of synthesis of data sets to be used as input for the modeling of concentrating solar power (CSP) plants, a simplified approach to the Olseth and Skartveit [4] scheme is applied here. 3,2 Probability distribution Probability densities of the k*dir values have been extracted from empirical data series. The k*dir values are calculated on the basis of a simple clear sky model, tuned to give a reasonable envelope of the measured DNI values. Fig. 1 shows an example for clear sky and measured minute by minute values for one day as derived for a site in Spain.. Fig.2: Series of measured DNI values with a time resolution of one minute together with a model for the values under clear sky condition. Characteristic probability distributions are gained for different sub ensembles formed by data of data from hours in different classes of hourly means of k*dir. A class width of.1 was selected. Fig. 3 gives the cumulative distributions of these sets. Fig: 3: Cumulative distributions of 1 minute values of the clear sky index (site:spain). Curves refer to different classes of the hourly mean clear sky index (color labeling given on the right). 3.3 Temporal characteristics The autocorrelation can be coefficient determined for empirical subsets as given as above. Values of the coefficient range from >.9 for hours with average clear sky value close to 1 to ~ for lower values of the hourly mean (see table 1). 3.4 Data synthesis Sets of synthetic DNI data are generated from synthetic k*dir sets together with the model of the clear sky DNI. The k*dir sets are synthesized using an autoregressive (AR1) process to establish a series with predetermined correlation characteristics. As this type of process results in a set of normally distributed values (with a target standard deviation of one), a subsequent mapping to a set with the desired probability distribution is necessary. This type of procedure is already established in schemes for the generation of sets of global clearness index data in different time domains. (see e.g. [1], [7]). The applicability of DNI series established by this procedure to the modeling of concentrated solar power systems (CSP) has been shown by [1]. Daily performance of a CSP system had been simulated using measured and simulated minute-by-minute data sets. The analysis of daily integrated performance figures gave satisfactory results. 4. COMBINED APPLICATION OF DATA CORRECTION AND ENHANCEMENT OF TIME RESOLUTION In this section, an application example for the combined application of the scheme discussed above is given. The measured data set used for this example is taken the BSRN data base. The site Tamanrasset is located in the Sahara desert (26 North). For the satellite derived data set a result from the Heliosat-3 procedure [8] is used. This specific set is constructed under incomplete knowledge of the atmospheric conditions, leading to remarkable deviations in the annual mean and distribution characteristics of measured and modeled DNI sets (fig.4)..

4 satellite data sets from the year 27 are taken as training period for the data correction. The data from the year 28 are used as test period. The application of the schemes discussed result in CDF s of the corrected set as given in fig.5 giving the data for the Month of January. Fig. 4: Cumulative distribution functions (CDF) of measured (blue) and satellite derived (green) hourly DNI data for the site Tamanrasset, January 28. For quality improvement, the parallel ground and Shown are, as before, the CDF for satellite derived DNI and hourly measurements, month of January. Added are the CDF s for hourly corrected satellite derived data- 'the correction based on the 27 data set, and the CDF s of minute-by-minute ground and corrected satellite data. For these sets, the hourly and minute-byminute CDF s are almost non-discernable. Fig. 5: Cumulative distribution functions of DNI sets for the site Tamanrasset, January 28. In addition to the curves shown in fig.4, the CDF for the satellite derived DNI data, as corrected using the error characteristics taken from parallel data of year 27 (dark green). The light blue line gives the empirical CDF from minute-by-minute data, the light green line the CDF for satellite derived DNI data with synthetically superimposed minute-by-minute data, This example shows, that even for data with a quietly remarkably distorted CDF, the correction method discussed can lead to a reasonable outcome. The transition to minute-by-minute data does not give a pronounced change in the CDF a property reflecting the conditions given by the measured sets. Fig.7 and Fig.8 show the CDF s of the minute-byminute measured and satellite corrected and enhanced sets for the month of April and September. For these month s the results of the satellite based CDF are again more closely to the empirical CDF. Fig.6 gives the same set of CDF s for the month of June. For this month, the correction of the minute-byminute sets from measured and corrected satellite data is less convincing. For both, the measured and satellite based sets, the deviations of the hurly and minute-byminute CDF s are a little more pronounced.

5 Fig. 6: Same presentation as fig.5, but for the month of June. 4. CONCLUSIONS Using appropriate methods, satellite derived DNI sets may be improved concerning the data quality and extended by synthetically enhanced time resolution when merging with information of confined parallel sets of ground measured DNI data. Prerequisite is the stability of the data processing scheme. Fig.7: Comparison of the CDF s of measured and satellite derived and processed minute-by-minute data sets for April REFERENCES. [1] Schenk,H., T.Hirsch, Dynamics of oil based parabolic trough plants a detailed transient simumlation model, Proceedings of the 21 SolarPACES conference, Perpignan, France (21) [2] Schumann, K., H.G. Beyer, R. Meyer, K. Chhatbar, Improving satellite-derived solar resource analysis with parallel ground-based measurements, ISES Solar World Congress 211, , Kassel, Germany (211) Fig.8: Comparison of the CDF s of measured and satellite derived and processed minute-by-minute data sets for September 28. [3] Beyer. H.G., M. Fauter, K. Schumann, H. Schenk, Richard Meyer, Syntheses of DNI time series with subhourly time resolution, Proceedings of the 21 SolarPACES conference, Perpignan, France (21) [4] Skartveit, A., Olseth, J.A., The probability density and autocorrelation of short-term global and beam irradiance. Solar Energy 46, (1992)

6 [5] Beyer,H.G., R. Meyer, K. Chhatbar, Postprocessing modellierter Einstrahlungszeitreihen zur Erstellung von Datensätzen mit realtätsnahen Verteilungseigenschaften, 2. Fachtagung Energiemeteorologie , Bremerhaven, Germany (211) [6] Walkenhorst, O., J. Luther, Ch. Reinhart, J. Timmer, Dynamic annual daylight simulation based on one-hour and one-minute means of irradiance data, Solar Energy 77, (22) [7] G. Becker, H.G. Beyer, M. Maier, S. Söltl, V. Quaschning, M. Zehner, Calculation of global weather data via JAVA applet in the internet, 17th European PV Solar Energy Conference, Munich, Germany (21)