International Journal of Sustainable Energy
ISSN: 1478-6451 (Print) 1478-646X (Online) Journal homepage: http://www.tandfonline.com/loi/gsol20
PV-GIS: a web-based solar radiation database for the calculation of PV potential in Europe Marcel Šúri , Thomas A. Huld & Ewan D. Dunlop To cite this article: Marcel Šúri , Thomas A. Huld & Ewan D. Dunlop (2005) PV-GIS: a web-based solar radiation database for the calculation of PV potential in Europe, International Journal of Sustainable Energy, 24:2, 55-67, DOI: 10.1080/14786450512331329556 To link to this article: http://dx.doi.org/10.1080/14786450512331329556
Published online: 01 Feb 2007.
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Date: 29 November 2015, At: 20:59
International Journal of Sustainable Energy Vol. 24, No. 2, June 2005, 55–67
PV-GIS: a web-based solar radiation database for the calculation of PV potential in Europe
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MARCEL ŠÚRI*, THOMAS A. HULD and EWAN D. DUNLOP European Commission, DG Joint Research Centre, Institute for Environment and Sustainability, Renewable Energies Unit, via E. Fermi 1, TP 450, I-21020 Ispra (VA), Italy Solar radiation is a key factor determining electricity produced by photovoltaic (PV) systems. This paper presents a solar radiation database of Europe developed in the geographical information system, and three interactive web applications providing an access to it. The database includes monthly and yearly average values of the global irradiation on horizontal and inclined surfaces, as well as climatic parameters needed for an assessment of the potential PV electricity generation (Linke atmospheric turbidity, the ratio of diffuse to global irradiation, an optimum inclination angle of modules to maximize energy yield). In the first web application, a user may browse radiation maps and query irradiation incident on a PV module for different inclination angles. The second application simulates daily profiles of irradiance for a chosen month and module inclination and orientation. The third web application estimates electricity generation for a chosen PV configuration. It also calculates optimal inclination and orientation of a PV module for a given location. The database and the applications are accessible at http://re.jrc.cec.ev.int/pvgis/pv/imaps/imaps.htm. Keywords: Solar radiation; Geographic information system; PV potential estimation; Web application; Map
1.
Introduction
It is anticipated that photovoltaic (PV) systems will experience an enormous increase within the next 10 years. However, a successful integration of solar energy technologies into the existing energy structure depends also on a detailed knowledge of the solar resource. Scaleup of PV systems in the energy market depends on public support schemes that should also consider climate variability within regions of Europe. Spatially distributed (map) databases help to understand the geographical and time distribution of the solar energy resource and the potential performance of the PV systems. The amount of incident solar radiation significantly determines the electricity produced by PV systems. The primary solar radiation data are measured at a limited number of climatic ground stations. To get maps, interpolation techniques are used, such as spline functions, weighted average procedures, kriging or co-kriging (Hutchinson et al. 1984, D’Agostino and Zelenka 1992, Zelenka et al. 1992, Hulme et al. 1995, Beyer et al. 1997). Methods of calculating solar irradiance from meteorological geostationary satellites (e.g., Meteosat, ∗ Corresponding
author. Email:
[email protected]
International Journal of Sustainable Energy ISSN 1478-6451 print/ISSN 1478-646X online © 2005 Taylor & Francis Ltd http://www.tandf.co.uk/journals DOI: 10.1080/14786450512331329556
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GOES) are rapidly developing (Cano et al. 1986, Müller et al. 2002, Perez et al. 2004). In general, processing of satellite data provides less accurate values for the location (compared with ground measurements), but the advantage is data coverage over vast territories at temporal resolutions of 0.5–12 h (Noia et al. 1993a,b). The new generation of satellites (such as Meteosat Second Generation, MSG) and new processing models (e.g., Heliosat-3, Müller et al. 2002) provide data at even higher spatial and temporal resolutions (grid cell size of 1 × 1 km, every 15 min one image for Meteosat-8) in operational regime, so that they can also be used for energy-weather forecasting and large scale PV monitoring. At present, various data sets are available, offering solar radiation and other climatic data, e.g., European Solar Radiation Atlas (ESRA) (Scharmer and Greif 2000) and MeteoNorm (Remund et al. 1999), distributed on CD-ROMs. Information on solar radiation and related parameters is also available on the Internet, see the web sites SoDa (Wald 2000, http:// www.soda-is.com), Satel-Light (Hammer et al. 1998, http://www.satellight.org), NASA SSE (http://eosweb.larc.nasa.gov/sse/) and NREL (http://rredc.nrel.gov/solar). Although these sources provide data with global or continental coverage, their spatial resolution is relatively coarse (1◦ × 1◦ for the NASA SSE, 5 × 5 ESRA, 40 × 40 km for the data from NREL). This coarse spatial resolution can lead to anomalous predictions, particularly in mountainous regions where data accuracy is lower. Strongly varying elevation gradients combined with terrain shadowing can cause significant local irradiance fluctuations. The availability of high resolution data sources (digital elevation models (DEMs), land cover, etc.) provides us with the means to improve the estimates of the regional solar radiation variations. To account for spatial variations of solar radiation in areas with dynamic terrain, solar radiation models integrated within geographical information systems (GIS) are helpful. Solar radiation models incorporate physically based and empirical equations to provide rapid and accurate estimates of radiation over large regions, while also considering surface inclination, orientation and shadowing effects. Coupling radiation models with GIS and image processing systems improves our ability to use different environmental and socio-economic data, to cooperate with other models and develop scenarios. In the previous work (Šúri and Hofierka 2004), we have analyzed GIS-based solar radiation models, such as SolarFlux (Dubayah and Rich 1995), Solei (Mészároš 1998), Solar Analyst (Fu and Rich 2000) and SRAD (Wilson and Gallant 2000). An analysis showed that these models have various restrictions in terms of their applicability for large territories and handling all necessary input parameters as spatially distributed data. Therefore, it was decided to develop and apply a new model, denoted as r.sun, which is discussed later. In this paper, we present a solar radiation database developed for Europe, using the solar radiation model r.sun within the GRASS GIS, and a set of web-based tools to enable data browsing, display and estimation of PV electricity production for any chosen locality. Geographically, the GIS database covers the European subcontinent (small parts of Azerbaijan and Russia are missing) and surrounding areas. The applied map projection is Lambert azimuthal, equal area. It is centered on 48◦ north latitude and 18◦ east longitude (∼60 km east of Bratislava, Slovakia), and the grid extent is 5000 km in the east-west direction and 4500 km in the northsouth direction. The resolution is 1 × 1 km2 ; this gives a total of 2.25 × 107 grid points for each data layer.
2.
Developing the solar radiation GIS database
Šúri and Hofierka (2004) have developed a GIS-based methodology for computation of solar irradiance/irradiation at a given surface inclination for any geographical region and for any
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time moment or interval. This approach has been implemented in the GIS software GRASS and it is based on use of the solar radiation model, denoted as r.sun, and the spatial interpolation techniques s.surf.rst and s.vol.rst (Neteler and Mitasova 2002). The algorithm of the model r.sun is conceptually based on the equations published in ESRA (Rigollier et al. 2000, Scharmer and Grief 2000). It estimates beam, diffuse and reflected components of the clear-sky and real-sky global irradiance/irradiation on horizontal or inclined surfaces. The total daily irradiation (Wh m−2 ) is computed by the integration of the irradiance values (W m−2 ) calculated at regular time intervals over the day. For each time-step during the day, the computation accounts for sky obstruction (shadowing) by local terrain features (hills or mountains), calculated from DEM. We present a brief explanation of the computational procedure. Details of the model equations and applied approach can be consulted in the paper by Šúri and Hofierka (2004). The main input parameters, used in the computation chain, were as follows: • monthly averages of daily sums of global Hhs and diffuse irradiation Hdhs , measured at 566 ground meteorological stations distributed over the region, representing the time period of 1981–1990 (source ESRA, Scharmer and Grief 2000); • linke turbidity TLK collected for 611 points at the SoDa web service (Wald 2000); • DEM with a grid resolution of 1 × 1 km, derived from the USGS GTOPO30 data (http://edcdaac.usgs.gov/gtopo30/gtopo30.html). In the first step, the point values of Linke turbidity TLK were interpolated by the GRASS module s.surf.rst that uses a two-dimensional regularized spline with tension (Mitasova and Mitas 1993). The accuracy of the data from the source is reported as root-mean square error (RMSE) = 0.7 TLK units. To eliminate the effect of elevation, the TLK values were normalized to the elevation at sea level before interpolation (World Meteorological Organization, 1981): TLKn = TLK + 0.00035z
(1)
where z is the local elevation, derived from DEM. The TLKn sites were interpolated using s.surf.rst. The 12 grid maps of TLK values, representing the monthly averages, were obtained from equation (1) using interpolated TLKn and elevation z. The solar radiation database was computed in three steps: (1) computation of clear-sky global irradiation on a horizontal surface Hhc ; (2) calculation and spatial interpolation of the clear-sky index kc , and computation of maps of global irradiation on a horizontal surface Hh ; (3) deriving the diffuse and beam components of the clear-sky index kcd and kcb , and computation of maps of global irradiation on inclined surfaces Hi . 2.1
Clear-sky global irradiation on a horizontal surface
The global irradiation consists of beam (direct), diffuse and reflected components. Horizontal global irradiation under clear-sky (cloudless) conditions Hhc (Wh m−2 ) is computed by the solar radiation model r.sun using elevation above sea level, latitude and Linke turbidity as input grid data layers. The day number and sun declinations for calculation of monthly averages were chosen according to the ESRA recommendations (Scharmer and Greif 2000). Twelve maps representing averages of the daily sums of clear-sky global irradiation Hhc were calculated by numerical integration of irradiances Ghc , with a time-step of 15 min.
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The clear-sky global irradiance Ghc for each time-step was calculated as the sum of its beam Gbhc and diffuse Gdhc components: Ghc = Gbhc + Gdhc
(2)
The clear-sky beam irradiance on a horizontal surface Gbhc (W m−2 ) was calculated as:
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Gbhc = G0 exp{−0.8662 TLK mδR (m)} sin h0
(3)
where G0 is the extraterrestrial irradiance normal to the solar beam, the term −0.8662 TLK is the air mass 2 Linke atmospheric turbidity. The parameter m is the relative optical air mass, the parameter δR (m) is the Rayleigh optical thickness at air mass m and h0 is the solar altitude angle (Kasten and Young 1989, Kasten 1996). The estimation of the clear-sky diffuse irradiance component on a horizontal surface Gdhc (W m−2 ) was made as the product of the normal extraterrestrial irradiance G0 , a diffuse transmission function Tn determined by the Linke turbidity TLK , and the diffuse solar altitude function Fd that is only dependent on the solar altitude angle h0 (Rigollier et al. 2000, Scharmer and Greif 2000). The estimate of the transmission function Tn(TLK ) gives a theoretical diffuse irradiance on a horizontal surface with the sun vertically overhead for the air mass 2 Linke turbidity factor: Gdhc = G0 Tn(TLK )Fd (h0 ) 2.2
(4)
Global irradiation on a horizontal surface
The real-sky horizontal global irradiation Hh (considering average cloudiness) for each month was calculated from clear-sky values Hhc using an empirical parameter quantifying the attenuation by cloud cover, the clear-sky index kc : Hh = Hhc kc
(5)
The clear-sky index kc expresses the ratio between monthly averages of global irradiation for real-sky and clear-sky conditions. For each meteorological station within Europe, a set of 12 monthly averages of the clear-sky index kc was calculated, by dividing the measured global irradiation Hhs by our calculated clear-sky values Hhc : kc =
Hhs Hhc
(6)
Grid data layers of kc were then obtained by interpolation from the station values using the multivariate regularized spline with tension (Šúri and Hofierka 2004). The method was developed by Hofierka et al. (2002) and implemented in GRASS GIS as the module s.vol.rst. 2.3
Global irradiation on an inclined surface
The components of the irradiance/irradiation are affected in different ways by cloudiness and shadowing by the terrain features, or by tilting the surface away from the horizontal position. Variation in any of these parameters will therefore change the ratio between the beam and diffuse components. For this reason, the real-sky beam and diffuse components of the horizontal irradiance (Gbh and Gdh ) are needed for calculation of radiation for a tilted
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surface. Within the computation of irradiances in each time-step, this was done by multiplying the clear-sky irradiances by the particular components of the clear-sky index kc : Gdh = Gdhc kcd
(7)
Gbh = Gbhc kcb The beam and diffuse components of the clear-sky index (kcb and kcd ) were derived from the monthly averages of global irradiation Hh (equation 5) and grid maps of the ratio of diffuse to global horizontal irradiation (Hdhs /Hhs ) that were spatially interpolated from available meteorological measurements using s.vol.rst.
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Hdh =
Hh Hdhs Hhs
(8)
Hbh = Hh − Hdh Hdh Hdhc Hbh kcb = Hbhc
kcd =
(9)
where subscript ‘s’ is meant to distinguish between diffuse Hdhs and global Hhs irradiation measured (or derived) at meteorological stations and our computed values Hdh and Hh . Afterwards, the daily sum of global irradiation on an inclined surface Hi (Wh m−2 ) for average real-sky atmospheric conditions was calculated for each month by numerical integration of the global irradiances Gi , also calculated with a time-step of 15 min. Gi = Gbi + Gdi + Gri
(10)
The beam irradiance on an inclined surface Gbi (W m−2 ) is derived from horizontal real-sky beam irradiance Gbh (Schramer and Grief 2000): Gbi =
Gbh sin δexp sin h0
(11)
where δexp is the solar incidence angle measured between the sun and an inclined surface. For the estimation of diffuse irradiance on an inclined surface Gdi for average cloudiness within each month (W m−2 ), we have applied the model by Muneer (1990). This model distinguishes between sunlit and shadowed surfaces, and further distinguishes between overcast and non-overcast conditions of the sunlit surface: (1) For sunlit surfaces under overcast sky and surfaces in shadow (δexp < 0 and h0 ≥ 0): Gdi = Gdh F (γ N )
(12)
(2) For sunlit surfaces under non-overcast sky (solar altitude h0 in radians): if h0 ≥ 0.1 (i.e., 5.7◦ ) Kb sin δexp Gdi = Gdh F (γ N )(1 − Kb ) + sin h0 if h0 < 0.1
Gdi = Gdh
Kb sin γ N cos ALN F (γ N )(1 − Kb ) + 0.1 − 0.008h0
(13)
(14)
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Here, γ N is the inclination angle of the surface and ALN is the angle between the vertical plane containing the normal to the surface and the vertical plane passing through the center of the solar disk. The function F (γ N ) accounts for the diffuse sky irradiance distribution and Kb is the fraction of the beam irradiance available (proportion between beam and extraterrestrial solar irradiances on a horizontal surface). The Gdh represents horizontal real-sky diffuse irradiance. The estimation of the ground reflected irradiance for inclined surfaces Gri relies on an isotropic assumption. The ground reflected real-sky irradiance received on an inclined surface (W m−2 ) is proportional to the global horizontal irradiance Gh , to the ground albedo ρg and a fraction of the ground viewed by an inclined surface rg (γ N ) (Scharmer and Greif 2000):
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Gri = ρg Gh rg (γ N )
(15)
The albedo varies depending on the ground surface (Page 1986), values of 0.2 or 0.15 are mostly used. The global irradiation at an inclined angle Hi was calculated again by numerical integration of irradiances Gi , with a time-step of 15 min. The basic data of the resulting GIS database represent 12 monthly and the yearly average values of the following climatic parameters: • daily sums of global irradiation on a horizontal surface Hh (Wh m−2 day−1 ); • daily sums of global irradiation for surfaces inclined at 15◦ , 25◦ , 40◦ and 90◦ Hi (Wh m−2 day−1 ); • ratio of diffuse to global irradiation Hdh /Hh ; • Linke turbidity coefficient (TLK ); • clear-sky index (kc ). In the calculation, the shadowing effect of the local terrain features was considered on the basis of the DEM. A number of modifications were made to the original approach by Šúri and Hofierka (2004). A set of optimizations of the original r.sun module yielded a significant speed-up to the calculations. It was found that the main part of the calculation effort was spent in determining the shadowing effect. A GRASS module, r.shadow, was therefore developed to calculate the shadowing effect separately. Another couple of GRASS modules, r.sunoptangle and r.sunyear, was developed to calculate the optimum inclination of a southwards-oriented surface (inclination of the surface at which the daily or yearly irradiation is maximum) with precision of 1◦ . The module r.sunoptangle calculates the optimum angle for a given day in the year, whereas r.sunyear calculates the optimum angle for the entire year (assuming a PV system with a fixed inclination angle). These calculations also take into account terrain shadowing derived from the elevation data. The programs output the optimum angle as well as the corresponding global irradiation values. As a result of these modifications, the following grid data were computed. • optimum surface inclination angle to maximize total irradiation input over a year (degrees); • monthly and yearly average values of the daily sums of global irradiation for surface at (locally) optimum surface inclination (Wh m−2 day−1 ). Our interpolation approach was compared with that of the ESRA project using the meteorological data from 566 stations. The RMSE error of our approach to the original data set for monthly horizontal irradiation is within the interval of 3.4–8.6%. The yearly average is 5.2% and the RMSE values peak in winter months (figure 1). The comparison of the ESRA interpolation approach shows that, although the overall accuracy is practically the same (the yearly average of the RSME for ESRA is 5.4%), the PV-GIS interpolation is slightly better in period from October to April and poorer in summer months. As the comparison was made
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Figure 1. Comparison of the monthly averages of the horizontal irradiation computed using the PV-GIS and ESRA approaches with the meteorological data at 566 stations, used in the calculation scheme (RMSE in absolute and relative values).
using the data set that was also used for the interpolation, this comparison only describes the accuracy in those points.
3. Web-based tools The GIS database in its primary design is useful only for a limited number of experts. To provide access to a more general public, it was decided to link the data with web-based interactive applications. The web interface is designed to give an overview of solar irradiation and PV performance data, in the form of maps and a series of graphs/tables at any location. A location can be chosen either by browsing/zooming and clicking on a map, choosing a country and city from a list, or by directly setting latitude/longitude values. The monthly and yearly values are displayed in a separate window. A basic geographical map, as well as a set of climatic and PV-potential estimation maps, enables the user to get an overview of the geographical variation of the data. The web interface is organized into three applications.
3.1
Querying maps of solar irradiation and related climatic data
This application provides monthly and yearly average values of global irradiation at horizontal and inclined surfaces, as well as other climatic and PV-related data (Linke turbidity, ratio diffuse/global irradiation and optimum inclination angle of the surface). Besides these parameters, for a chosen position, an estimation of the deficit in yearly horizontal irradiation due to terrain shadowing is also presented.
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3.2 Estimation of the (average) daily irradiance variation For a selected module inclination and orientation, a user can get a daily profile of clear-sky and real-sky irradiances for a chosen month. The daily variance is estimated by a standalone calculator, running on a server. The calculator takes into account also the shadowing by local terrain features. 3.3 Estimation of PV potential
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This application calculates the yearly potential electricity generation E(kWh) of a PV configuration with defined modules inclination and orientation using a formula: E = 365Pk rp Hh,i
(16)
where Pk (kW) is the peak power installed, rp is the system performance ratio (typical value for roof mounted system with modules from mono- or polycrystalline silicon is 0.75) and Hh,i is the monthly average or yearly average value of daily global irradiation on the horizontal or inclined surface. The calculator can suggest the optimum inclination/orientation of the PV modules to harvest maximum electricity within a year. For all three applications, the maps have been calculated beforehand, whereas the calculations of the data for a single (user-defined) location are performed upon request. Since these calculations are rather computing-intensive, a number of steps have been implemented to optimize the calculation speed: • All calculations are performed on the web server. Thus, the calculation times do not depend on the speed of the users’ computer. • Calculations are performed by standalone programs written in C language. This makes it much faster than if the program was written in one of the normal scripting languages for the web (such as PHP or Perl). • The necessary data for the calculations are stored in binary form, with a constant record length for the data for each grid point. In this way, the reading of input data is an O(1) operation, independent of the size of the data set. For the PV potential estimation, the user has an option to ask for a calculation of the optimum inclination and orientation (in the east-west direction). The total amount of data stored at the web site is ∼7 GB. Apart from the programs calculating the irradiance and irradiation, most parts of the web applications are written using server-side scripting, primarily PHP. The use of client-side scripting (JavaScript) is kept to a minimum to allow users with limited computing resources to use the site.
4.
Results
In comparison with other available data sources, the developed GIS database offers consistent information about solar irradiation and related parameters for Europe and surrounding territories. The level of spatial detail is given by the grid resolution of 1 × 1 km. The monthly and yearly average values of the global irradiation represent the period 1981–1990. An innovative aspect is also the incorporation of shadowing effects of the local terrain features that modify the spatio–temporal patterns of the radiation fields. The maps reveal significant regional differences in global irradiation, determined by latitude, terrain and local climatic conditions. According to the results, the 10-years average of daily
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totals of horizontal irradiation at unshadowed conditions in the European subcontinent ranges from 1850 (near North Cape in Arctic Norway) to 5000 Wh m−2 (Southeast of Granada, Spain; 2.7 times more than in Northern Norway, figure 2). Inclining the solar modules to harvest maximum global irradiation within a year, increases average daily totals to 2300 and 5800 Wh m−2 , respectively (2.5 times more in Spain). As presented in figure 3, the optimum inclination angle of the unshadowed PV modules within Europe ranges from 26◦ in Southern Europe to 51◦ in Northern Scandinavia. The terrain shadowing in the mountains can locally decrease optimum inclination to almost horizontal position. The web applications enable an easy and user-friendly access to the data and simple estimations of the potential PV electricity generation for a given location and PV system configuration. They are meant as a concise reference and information source to support decision-makers, researchers and industry in addressing national/regional PV developmental and marketing plans. The data can also contribute to education and will promote public awareness of the potential for solar energy. Figures 4–6 show examples of the type of graphics output that the web site can produce for a given geographical location. Figure 4 shows the monthly variation of the average daily irradiation on a horizontal surface, for a location in the Italian Alps. Figure 5 shows daily irradiance variation for the same location for the month of February. It is clearly seen how the global irradiance is reduced to its diffuse component when the direct component is shadowed by the mountains. Finally, in figure 6 the estimated output of a 1 kWp PV system is presented for the same location. The inclination and orientation of the PV system were calculated by the
Figure 2.
Map of yearly average of daily total of global irradiation on a horizontal surface.
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Figure 3. Map of the optimal inclination angle for a south-facing surface, over the European subcontinent. The optimal inclination angle is the angle at which the surface receives the largest amount of total yearly global irradiation. For locations with strong terrain shadowing, the optimal angle is much lower.
Figure 4. Graph of monthly variation of the average daily global irradiation on horizontal and inclined surfaces, for a location near Macugnaga, in the Italian Alps, as calculated by the solar radiation web application.
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Figure 5. Graph of daily global irradiance variation on a south-facing surface inclined at 45 (for the same location as in figure 3 (Macugnaga, Italy). Irradiance is shown for both average clear-sky and real-sky conditions for February considering the local solar time. Note the jumps in irradiance due to shadows from nearby mountains, where the irradiance falls to the value of its diffuse component.
Figure 6. Graph of the estimated monthly electricity production from a PV system, as calculated by the PV estimation web application for the same location as in figures 3 and 4. The graph shows the monthly production for a 1 kWp system with a performance ratio of 0.75. The inclination is 36◦ and the orientation is 10◦ away from due south in the east direction. These values were calculated by the application for the optimal values to maximize yearly power production. The horizontal line in the graph represents the average monthly production during the year.
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web application to maximize yearly electricity production. Note that for a mountain location such as this, the optimal orientation may deviate from due south. The applications are located at http://re.jrc.cec.ev.int/pvgis/pv/imaps/imaps.htm and include support pages explaining the applied data sources, methodology and obtained accuracy.
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5.
Discussion and conclusions
GIS integrates the technologies for acquisition and processing of the spatially distributed data. The multidimensional interpolation techniques in GIS, such as s.vol.rst, make it possible to take into account the dynamic nature of interactions between solar radiation, atmosphere and the earth surface. With GIS, it is possible to combine the primary solar resource with other types of data, to analyze regional differences and socio-economic impacts, and to present results in the form of maps. The integration of ground measurements, satellite data and solar radiation models within GIS enables to effectively calculate map information from point measurements within the whole European subcontinent at much lower costs when compared with the alternative of having a more dense network of meteorological ground stations. With the presented approach, it is possible to develop a solar radiation database for any geographical region. The solar radiation model r.sun is very effective for fast estimations over large areas with complex terrain because it uses all the input parameters as GIS grid maps. The spatial detail and temporal resolution of the database depend only on the available DEM and climatic data. The implemented model equations follow the latest European research in solar radiation modeling. The application of the solar radiation model for regions outside of Europe is rather limited as the equations for diffuse radiation are validated on European climatic data. However, given an appropriate diffuse model (e.g., for tropical conditions), the developed GIS modules and the web applications could be applied to any geographical region. Calculation of the radiation deficit caused by terrain shadowing is determined by the spatial resolution of the DEM used and should be considered as an approximation. Our experiments have shown that applying high resolution DEM (e.g., 100 × 100 m) can dramatically improve the spatial accuracy of the shadowing. If solar radiation and related climatic measurements were available with higher temporal resolution (e.g., daily and hourly values), the presented approach could be used to estimate solar radiation dynamics at high spatial and temporal resolutions. Additionally, the presently available data from geostationary satellites (e.g., Meteosat-8) provide valuable inputs to the presented approach to generate maps of historical, actual or predicted solar radiation and the PV energy production assessments on a local or regional basis. The data from the database are available online for any given location, or upon request they can be delivered as GIS data layers. The data, maps, animations and the supporting information published on the web can be used as a reference or supplement to other data sources for policymakers, industry, owners of PV systems as well as for the broad public. Work continues on improving the accuracy and incorporating further data. Acknowledgments This work has been carried out under the European Commission Joint Research Centre Enlargement Action Program as project number 52 entitled ‘Environment and the Solar Energy Resource’. The authors would like to thank to Jaroslav Hofierka, Tomas Cebecauer and Arwyn R. Jones for the discussions and helpful comments.
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