Framework for the mapping of the monthly average daily solar radiation using an advanced case-based reasoning and a geostatistical technique

Environmental Science and Technology

Volumn 48, Issue 8, 2014, Pages 4604-4612

  Lee, Minhyun ^a   Koo, Choongwan ^a   Hong, Taehoon ^a   Park, Hyo Seon ^a  

^a Yonsei University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

GEOGRAPHIC INFORMATION SYSTEMS; MAPPING; METEOROLOGY; SOLAR RADIATION; SUN;

CASEBASED REASONINGS (CBR); DAILY SOLAR RADIATIONS; DECISION MAKING PROCESS; GEOSTATISTICAL TECHNIQUES; METEOROLOGICAL DATA; OPTIMAL LOCATIONS; PHOTOVOLTAIC SYSTEMS; SOUTH KOREA;

LOCATION;

ACCURACY ASSESSMENT; DATA SET; DECISION MAKING; GEOSTATISTICS; GIS; INSTALLATION; MAPPING METHOD; METEOROLOGY; NUMERICAL MODEL; PHOTOVOLTAIC SYSTEM; SOLAR RADIATION;

ADVANCED CASE BASED REASONING; ARTICLE; CONCEPTUAL FRAMEWORK; DECISION MAKING; GEOGRAPHIC INFORMATION SYSTEM; GEOSTATISTICAL ANALYSIS; KRIGING; METEOROLOGICAL PHENOMENA; MONTHLY AVERAGE DAILY SOLAR RADIATION; PHOTOVOLTAIC SYSTEM; PREDICTION; RADIATION MEASUREMENT; RENEWABLE ENERGY; SOLAR RADIATION; SOUTH KOREA;

GEOGRAPHIC INFORMATION SYSTEMS; MODELS, THEORETICAL; REPRODUCIBILITY OF RESULTS; REPUBLIC OF KOREA; SEASONS; SUNLIGHT;

EID: 84898869006     PISSN: 0013936X     EISSN: 15205851     Source Type: Journal    
DOI: 10.1021/es405293u     Document Type: Article

References (66)

1. Climate Change 2007: Synthesis Report; Intergovernmental Panel on Climate Change (IPCC), 2007.
2. Kyoto Protocol to the United Nations Framework Convention on Climate Change; United Nations (UN), 1998.
3. Koo, C.; Hong, T.; Lee, M.; Park, H. Estimation of the monthly average daily solar radiation using geographic information system and advanced case-based reasoning Environ. Sci. Technol. 2013, 47, 4829-4839
4. Koo, C.; Park, S.; Hong, T.; Park, H. An estimation model for the heating and cooling demand of a residential building with a different envelope design using the finite element method Appl. Energy 2014, 115 (15) 205-215
5. Kim, J.; Hong, T.; Koo, C. Economic and environmental evaluation model for selecting the optimum design of green roof systems in elementary schools Environ. Sci. Technol. 2012, 46 (15) 8475-8483
6. 2 analysis of green roofs in elementary schools with energy saving measures Energy Buildings 2012, 45 (2) 229-239
7. World Energy Outlook; International Energy Agency (IEA): Paris, France, 2012.
8. Clean Energy Progress Report; International Energy Agency (IEA): Paris, France, 2011.
9. Hong, T.; Koo, C.; Kwak, T.; Park, H. An economic and environmental assessment for selecting the optimum new renewable energy system for educational facility Renew. Sust. Energy Rev. 2014, 29 (1) 286-300
10. Hong, T.; Koo, C.; Kwak, T. Framework for the implementation of a new renewable energy system in an educational facility Appl. Energy 2013, 103 (3) 539-551
11. Renewable Energy Market and Policy Trends in IEA Countries; International Energy Agency (IEA): France, 2009.
12. Renewables 2011: Global Status Report; Renewable Energy Policy Network for the 21st Century (REN21): Paris, France, 2011.
13. Trends in Photovoltaic Applications: Survey Report of Selected IEA Countries between 1992 and 2011; International Energy Agency PVPS (IEA PVPS): Switzerland, 2012.
14. 2010 Solar Technologies Market Report; National Renewable Energy Laboratory (NREL): Colorado, USA, 2011.
15. Global Market Outlook for Photovoltaics until 2015; European Photovoltaic Industry Association (EPIA): Brussels, Belgium, 2011.
16. Hong, T.; Koo, C.; Lee, M. Estimating the loss ratio of solar photovoltaic electricity generation through stochastic analysis J. Constr. Eng. Project Manage. 2013, 3 (3) 23-34 (Special Issue: Best Paper Award and Selected papers from the 5th International Conference on Construction Engineering and Project (ICCEPM 2013)).
17. Koo, C.; Hong, T.; Park, H.; Yun, G. Framework for the analysis of the potential of the rooftop photovoltaic system to achieve the net-zero energy solar buildings. Prog. Photovolt.: Res. Appl. 2014, in press
18. Geraldi, E.; Romano, F.; Ricciardelli, E. An advanced model for the estimation of the surface solar irradiance under all atmospheric conditions using MSG/SEVIRI data IEEE Trans. Geosci. Remote Sens. 2012, 50 (8) 2934-2953
19. Badescu, V. Correlations to estimate monthly mean daily solar global radiation: Application to Romania Energy 1999, 24 (10) 883-893
20. Chegaar, M.; Chibani, A. Global solar radiation estimation in Algeria Energy Convers. Manage. 2001, 42 (8) 967-973
21. Ashhab, M. S. S. Optimization and modeling of a photovoltaic solar integrated system by neural networks Energy Convers. Manage. 2008, 49 (11) 3349-3355
22. Reddy, K. S.; Ranjan, M. Solar resource estimation using artificial neural networks and comparison with other correlation models Energy Convers. Manage. 2003, 44 (15) 2519-2530
23. Cano, D.; Monget, J. M.; Albuission, M.; Guillard, H.; Regas, N.; Wald, L. A method for the determination of the global solar radiation from meteorological satellite data Solar Energy 1986, 37 (1) 31-39
24. Gastli, A.; Charabi, Y. Solar electricity prospects in Oman using GIS-based solar radiation maps Renew. Sust. Energy Rev. 2010, 14, 790-797
25. Mohandes, M.; Rehman, S.; Halawani, T. O. Estimation of global solar radiation using artificial neural networks Renew. Energy 1998, 14 (1-4) 179-184
26. Alawi, S. M.; Hinai, H. A. An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation Renew. Energy 1998, 14 (1-4) 199-204
27. Dorvio, A. S. S.; Jervase, J. A.; Al-Lawati, A. Solar radiation estimation using artificial neural networks Appl. Energy 2002, 71 (4) 307-319
28. Sozen, A.; Arcakĺogblub, E.; Ozalpa, M.; Agclarc, N. C. Forecasting based on neural network approach of solar potential in Turkey Renew. Energy 2005, 30 (7) 1075-1090
29. Mubiru, E. J.; Banda, K. B. Estimation of monthly average daily global solar radiation using artificial neural networks Solar Energy 2008, 82 (2) 181-187
30. Behrang, M. A.; Assareh, E.; Ghanbarzadeh, A.; Noghrehabadi, A. R. The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data Solar Energy 2010, 84 (8) 1468-1480
31. Súri, M.; Hofierka, J. A new GIS-based solar radiation model and its application to photovoltaic assessments Trans. GIS 2004, 8 (2) 175-190
32. Janjai, S.; Pankaewa, P.; Laksanaboonsong, J.; Kitichantaropas, P. Estimation of solar radiation over Cambodia from long-term satellite data Renew. Energy 2011, 36 (4) 1214-1220
33. Pundt, H.; Brinkkotter-Runde, K. Visualization of spatial data for field based GIS Comput. Geosci. 2000, 26 (1) 51-56
34. Latu, S. Sustainable development: The role of GIS and visualisation Electron. J. Inform. Syst. Dev. Countries 2000, 38 (5) 1-17
35. Rehmana, S.; Ghori, S. G. Spatial estimation of global solar radiation using geostatistics Renew. Energy 2000, 21 (3-4) 583-605
36. Li, J. Spatial distribution of incoming potential solar radiation based on Solar Analyst model and DEM in Xinjiang, China Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses 2008, 71462M1-71462M8
37. Pons, X.; Ninyerola, M. Mapping a topographic global solar radiation model implemented in a GIS and refined with ground data Int. J. Climatol. 2008, 28 (13) 1821-1834
38. Sozen, A.; Arcaklioglu, E.; Ozalp, M.; Kanit, E. G. Use of artificial neural networks for mapping of solar potential in Turkey Appl. Energy 2004, 77 (3) 273-286
39. Fadare, D. A. Modelling of solar energy potential in Nigeria using an artificial neural network model Appl. Energy 2009, 86 (9) 1410-1422
40. Rumbayan, M.; Abudureyimu, A.; Nagasaka, K. Mapping of solar energy potential in Indonesia using artificial neural network and geographical information system Renew. Sust. Energy Rev. 2012, 16 (3) 1437-1449
41. Cogliani, E.; Ricchiazzi, P.; Maccari, A. Generation of operational maps of global solar irradiation on horizontal plan and of direct normal irradiation from Meteosat imagery by using SOLARMET Solar Energy 2008, 82 (6) 556-562
42. Janjai, S. A method for estimating direct normal solar irradiation from satellite data for a tropical environment Solar Energy 2010, 84 (9) 1685-1695
43. Janjai, S.; Laksanaboonsong, J.; Nunez, M.; Thongsathitya, A. Development of a method for generating operational solar radiation maps from satellite data for a tropical environment Solar Energy 2005, 78 (6) 739-751
44. Hu, Z. M.; Wu, J. P.; Wu, B.; Shu, S.; Yu, B. L. Simulating and mapping the variations of solar radiation at the Lujiazui region of Shanghai using airborne LiDAR data Key Eng. Mater. 2012, 500, 511-516
45. Mohandes, M.; Rehman, S. Global Solar Radiation Maps of Saudi Arabia. 2010 International Conference on Renewable Energy (ICRE) 2010.
46. Herbst, D. Solar mapping: Demystifying solar potential. Renew. Energy Focus, 2009, July/August, pp 32-35.
47. Winslow, J. C.; Raymond Hunt, E., Jr; Piper, S. C. A globally applicable model of daily solar irradiance estimated from air temperature and precipitation data Ecol. Model. 2001, 143, 227-243
48. Thornton, P. E.; Running, S. W. An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation Agric. Forest Meteorol. 1999, 93, 211-228
49. Löpez, G.; Batlles, F. J.; Tovar-Pescador, J. Selection of input parameters to model direct solar irradiance by using artificial neural networks Energy 2005, 30, 1675-1684
50. Hong, T.; Koo, C.; Kim, H.; Park, H. Decision support model for establishing the optimal energy retrofit strategy for existing multi-family housing complexes Energy Policy 2014, 66, 157-169
51. Hong, T.; Koo, C.; Lee, S. Benchmarks as a tool for free allocation through comparison with similar projects: Focused on multi-family housing complex Appl. Energy 2014, 114 (2) 663-675
52. 2 emission from electricity consumption J. Environ. Manage. 2012, 112 (15) 67-78
53. Hong, T.; Koo, C.; Jeong, K. A decision support model for reducing electric energy consumption in elementary school facilities Appl. Energy 2012, 95 (7) 253-266
54. 2 emission from gas energy consumption Building Environ. 2012, 52 (6) 142-151
55. Koo, C.; Hong, T.; Hyun, C. The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach Int. J. Expert Syst. Appl. 2011, 38 (7) 8597-8606
56. Koo, C.; Hong, T.; Hyun, C.; Park, S.; Seo, J. A study on development of a cost model based on the owner's decision making at the early stages of a construction project Int. J. Strat. Property Manage. 2010, 14 (2) 121-137
57. Koo, C.; Hong, T.; Hyun, C.; Koo, K. A CBR-based hybrid model for predicting a construction duration and cost based on project characteristics in multi-family housing projects Can. J. Civil Eng. 2010, 37 (5) 739-752
58. Burrough, P. A.; McDonnell, R. A. Principles of Geographical Information Systems; Oxford University Press: Oxford, U.K., 1998.
59. Johnston, K.; Ver Hoef, J.; Krivoruchko, K.; Lucas, N. Using ArcGIS Geostatistical Analyst; ESRI: USA, 2001.
60. McCoy, J.; Johnston, K. Using ArcGIS Spatial Analyst; ESRI: USA, 2001.
61. GIS Interpolation (IDW, Kriging); http://www.biz-gis.com/index.php? documentsrl=12477&mid=paper.
62. Inverse Distance Weighting (IDW) from Wikipedia, the free encyclopedia; http://en.wikipedia.org/wiki/Inversedistanceweighting.
63. Kriging from Wikipedia, the free encyclopedia; http://en.wikipedia.org/ wiki/Kriging.
64. Monthly Weather Report (from January 2004 to December 2011); Korea Meteorological Administration (KMA): Seoul, South Korea, 2004-2011.
65. The Geography of Korea; National Geographic Information Institute (NGII): Suwon, South Korea, 2010.
66. The Climate Atlas of Korea (1981-2010); Korea Meteorological Administration (KMA): Seoul, South Korea, 2012.