Abstract
Geographic information systems provide different options to analyze and represent the spatial heterogeneity of solar radiation incident on a certain area. This chapter presents a description of the main and well-known methods for determining interpolation surfaces from a data sample. Moreover, using 3D model of the analyzed area, computer models of spatial analysis are precise techniques to adjust the results to the variability of surfaces in a geographic area. Both alternatives offer a great analysis capacity. The selection of a procedure will depend on the objective of the study and the available information.
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References
Abramowitz M, Stegun IA (1974) Handbook of mathematical functions, with formulas, graphs, and mathematical tables. Dover Publications Inc, New York
Agencia Estatal de MeteorologÃa AEMET (2011) Agencia Estatal de MeteorologÃa. http://www.aemet.es/es/portada. Accessed 29 Mar 2018
Antonanzas J, Urraca R, Martinez-de-Pison FJ, Antonanzas-Torres F (2015) Solar irradiation mapping with exogenous data from support vector regression machines estimations. Energy Convers Manage 100:380–390. https://doi.org/10.1016/j.enconman.2015.05.028
Apaydin H, Kemal SF, Yildirim YE (2004) Spatial interpolation techniques for climate data in the GAP region in Turkey. Clim Res 28:31–40
Brito MC, Gomes N, Santos T, Tenedório JA (2012) Photovoltaic potential in a Lisbon suburb using LiDAR data. Sol Energy 86:283–288
Burrough PA, McDonnell RA (1998) Principles of geographical information systems. Oxford University Press, Oxford
Chilès J-P, Delfiner P (2012) Geostatistics: modeling spatial uncertainty. Willey Series in Probability and Statistics. Wiley, Hoboken
Cooper PI (1969) The absorption of radiation in solar stills. Sol Energy 12:333–346. https://doi.org/10.1016/0038-092X(69)90047-4
Cressie N (2015) Statistics for spatial data, revised edition. Wiley Classic Library. Wiley, Hoboken
Duffie JA, Beckman WA (2013) Solar engineering of thermal processes. Wiley-Interscience, Hoboken
Esri (2017a) ArcGIS desktop. Tools: an overview of the solar radiation toolset. http://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-analyst-toolbox/an-overview-of-the-spatial-analyst-toolbox.htm. Accessed 29 Mar 2018
Esri (2017b) ArcGIS Pro. Tool reference: cross validation. https://pro.arcgis.com/en/pro-app/tool-reference/geostatistical-analyst/cross-validation.htm. Accessed 29 Mar 2018
Esri (2018) Esri: GIS mapping software, spatial data analytics and location plarform. https://www.esri.com/es-es/home. Accessed 29 Mar 2018
European Commission (2012) Photovoltaic geographical information system interactive maps (PVGIS). http://re.jrc.ec.europa.eu/pvgis/apps4/pvest.php?lang=es&map=europe. Accessed 29 Mar 2018
Evrendilek F, Ertekin C (2008) Assessing solar radiation models using multiple variables over Turkey. Clim Dyn 31:131–149. https://doi.org/10.1007/s00382-007-0338-6
Fogl M, Moudrý V (2016) Influence of vegetation canopies on solar potential in urban environments. Appl Geogr 66:73–80. https://doi.org/10.1016/j.apgeog.2015.11.011
Fu P, Rich PM (1999) Design and implementation of the solar analyst: an ArcView extension for modeling solar radiation at landscape scales. In: Proceedings of the 19th annual ESRI user conference, San Diego, USA
Fu P, Rich PM (2000) The solar analyst 1.0 user manual. Helios Environmental Modeling Institute (HEMI), USA
Goovaerts P (1997) Geostatistics for natural resources evaluation. Applied geostatistics series. Oxford University Press, New York
Gopinathan KK, Soler A (1995) Diffuse radiation models and monthly-average, daily, diffuse data for a wide latitude range. Energy 20:657–667. https://doi.org/10.1016/0360-5442(95)00004-Z
GRASS Development Team (2018) GRASS GIS. https://grass.osgeo.org/. Accessed 29 Mar 2018
Hofierka J, Šúri M, Huld T (2007) r.sun. Solar irradiance and irradiation model. https://grass.osgeo.org/grass75/manuals/r.sun.html. Accessed 29 Mar 2018
Instituto Geográfico Nacional IGN (2015) Modelos Digital del Terreno - MDT25. Hoja MTN50 0485. http://www.ign.es/web/ign/portal. Accessed 29 Mar 2018
Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, New York
Johnston K, Ver Hoef JM, Krivoruchko K, Lucas N (2001) ArcGIS 9. Using ArcGIS geostatistical analyst. ESRI, Redlands, CA, USA
Jolly WM, Graham JM, Michaelis A, Nemani R, Running SW (2005) A flexible, integrated system for generating meteorological surfaces derived from point sources across multiple geographic scales. Environ Model Softw 20:873–882
Klein SA (1977) Calculation of monthly average insolation on tilted surfaces. Sol Energy 19:325–329. https://doi.org/10.1016/0038-092X(77)90001-9
Kodysh JB, Omitaomu OA, Bhaduri BL, Neish BS (2013) Methodology for estimating solar potential on multiple building rooftops for photovoltaic systems. Sustain Cities Soc 8:31–41
Mavromatidis G, Orehounig K, Carmeliet J (2015) Evaluation of photovoltaic integration potential in a village. Sol Energy 121:152–168. https://doi.org/10.1016/j.solener.2015.03.044
Meteotest (2017) Meteonorm. Global irradiation and climate data. https://meteotest.ch/en/. Accessed 29 Mar 2018
Mitášová H, Mitáš L (1993) Interpolation by regularized spline with tension: I. Theory and implementation. Math Geol 25:641–655. https://doi.org/10.1007/BF00893171
Oloo FO, Olang L, Strobl J (2015) Spatial modelling of solar energy potential in Kenya. Int J Sustain Energy Plann Manage 6:17–30. https://doi.org/10.5278/ijsepm.2015.6.3
Orgill JF, Hollands KGT (1977) Correlation equation for hourly diffuse radiation on a horizontal surface. Sol Energy 19:357–359. https://doi.org/10.1016/0038-092X(77)90006-8
Perea-Moreno A-J, Hernandez-Escobedo Q (2016) Solar resource for urban communities in the Baja California Peninsula, Mexico. Energies 9 https://doi.org/10.3390/en9110911
Righini R, Grossi Gallegos H, Raichijk C (2005) Approach to drawing new global solar irradiation contour maps for Argentina. Renew Energy 30:1241–1255. https://doi.org/10.1016/j.renene.2004.10.010
Santos Preciado JM, GarcÃa Lázaro FJ (2008) Análisis estadÃstico de la información geográfica. Cuadernos UNED. Universidad Nacional de Educación a Distancia, UNED, Madrid
Slocum TA, McMaster RB, Kessler FC, Howard HH (2014) Thematic cartography and geovisualization: pearson new international edition. Thematic cartography and geovisualization. Pearson Education Limited, London
Sun YW, Hof A, Wang R, Liu J, Lin YJ, Yang DW (2013) GIS-based approach for potential analysis of solar PV generation at the regional scale: a case study of Fujian Province. Energy Policy 58:248–259
Šúri M, Hofierka J (2004) A new GIS-based solar radiation model and its application to photovoltaic assessments. Trans GIS 8:175–190. https://doi.org/10.1111/j.1467-9671.2004.00174.x
Tooke TR, Coops NC, Voogt JA, Meitner MJ (2011) Tree structure influences on rooftop-received solar radiation. Landscape Urban Planning 102:73–81
Vicente-Serrano SM, Saz-Sánchez MA, Cuadrat JM (2003) Comparative analysis of interpolation methods in the middle Ebro Valley (Spain): application to annual precipitation and temperature. Climate Res 24:161–180
Wackernagel H (2003) Multivariate geostatistics: an introduction with applications. Springer, Berlin
Wong MS et al (2016) Estimation of Hong Kong’s solar energy potential using GIS and remote sensing technologies. Renew Energy 99:325–335. https://doi.org/10.1016/j.renene.2016.07.003
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MartÃn, A.M., Dominguez, J. (2019). Solar Radiation Interpolation. In: Polo, J., MartÃn-Pomares, L., Sanfilippo, A. (eds) Solar Resources Mapping. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-97484-2_8
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