Development of the monthly average daily solar radiation map using A-CBR, FEM, and kriging method
Abstract
Photovoltaic (PV) system could be implemented to mitigate global warming and lack of energy. To maximize its effectiveness, the monthly average daily solar radiation (MADSR) should be accurately estimated, and then an accurate MADSR map could be developed for final decision-makers. However, there is a limitation in improving the accuracy of the MADSR map due to the lack of weather stations. This is because it is too expensive to measure the actual MADSR data using the remote sensors in all the sites where the PV system would be installed. Thus, this study aimed to develop the MADSR map with improved estimation accuracy using the advanced case-based reasoning (A-CBR), finite element method (FEM), and kriging method. This study was conducted in four steps: (i) data collection; (ii) estimation of the MADSR data in the 54 unmeasured locations using the A-CBR model; (iii) estimation of the MADSR data in the 89 unmeasured locations using the FEM model; and (iv) development of the MADSR map using the kriging method. Compared to the previous MADSR map, the proposed MADSR map was determined to be improved in terms of its estimation accuracy and classification level.
First published online: 03 May 2017
Keyword : monthly average daily solar radiation, solar radiation map, advanced case-based reasoning, finite element method, kriging method
How to Cite
Koo, C., Hong, T., Jeong, K., & Kim, J. (2018). Development of the monthly average daily solar radiation map using A-CBR, FEM, and kriging method. Technological and Economic Development of Economy, 24(2), 489–512. https://doi.org/10.3846/20294913.2016.1213198
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Badescu, V. 1999. Correlations to estimate monthly mean daily solar global irradiation: application to Romania, Energy 24(10): 883–893. https://doi.org/10.1016/S0360-5442(99)00027-4
Bae, M. S. 2008. A runoff analysis by kriging method for the Nam River dam basin: Doctoral thesis. Gyeongsang National University, South Korea.
Behrang, M. A.; Assareh, E.; Ghanbarzadeh, A.; Noghrehabadi, A. R. 2010. The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data, Solar Energy 84(8): 1468–1480. https://doi.org/10.1016/j.solener.2010.05.009
Caglayan, N.; Ertekin, C.; Evrendilek, F. 2014. Spatial viability analysis of grid-connected photovoltaic power systems for Turkey, International Journal of Electrical Power and Energy Systems 56: 270–278. https://doi.org/10.1016/j.ijepes.2013.11.019
Cano, D.; Monget, J. M.; Albuisson, M.; Guillard, H.; Regas, N.; Wald, L. 1986. A method for the deter¬mination of the global solar radiation from meteorological satellite data, Solar Energy 37(1): 31–39. http://www.sciencedirect.com/science/article/pii/0038092X86901040
Casadei, F.; Gabellini, E. 1997. Implementation of a 3D coupled spectral element/finite element solver for wave propagation and soil-structure interaction simulations. Italy, European Commission.
Chegaar, M.; Chibani, A. 2001. Global solar radiation estimation in Algeria, Energy Conversion and Management 42(8): 967–973. https://doi.org/10.1016/S0196-8904(00)00105-9
Cogliani, E.; Ricchiazzi, P.; Maccari, A. 2008. Generation of operational maps of global solar irradiation on horizontal plan and of direct normal irradiation from Meteosat imagery by using SOLARMET, Solar Energy 82(6): 556–562. https://doi.org/10.1016/j.solener.2007.11.002
Coskun, C.; Oktay, Z.; Dincer, I. 2001. Estimation of monthly solar radiation distribution for solar energy system analysis, Energy 36(2): 1319–1323. https://doi.org/10.1016/j.energy.2010.11.009
Gastli, A.; Charabi, Y. 2010. Solar electricity prospects in Oman using GIS-based solar radiation maps, Renewable and Sustainable Energy Reviews 14(2): 790–797. https://doi.org/10.1016/j.rser.2009.08.018
Geraldi, E.; Romano, F.; Ricciardelli, E. 2012. An advanced model for the estimation of the surface solar irradiance under all atmospheric conditions using MSG/SEVIRI data, IEEE Transactions on Geoscience and Remote Sensing 50(8): 2934–2953. https://doi.org/10.1109/TGRS.2011.2178855
Hammer, A.; Heinemann, D.; Hoyer, C.; Kuhlemann, R.; Lorenz, E.; Müller, R.; Beyer, H. G. 2003. Solar energy assessment using remote sensing technologies, Remote Sensing of Environment 86(3): 423–432. https://doi.org/10.1016/S0034-4257(03)00083-X
Holt, M.; Campbell, R. J.; Nikitin, M. B. 2012. Fukushima nuclear disaster. Congressional Research Service (CRS).
Hong, T.; Koo, C.; Jeong, K. 2012a. A decision support model for reducing electric energy consumption in elementary school facilities, Applied Energy 95: 253–266. https://doi.org/10.1016/j.apenergy.2012.02.052
Hong, T.; Koo, C.; Kim, D.; Lee, M.; Kim, J. 2015. An estimation methodology for the dynamic opera¬tional rating of a new residential building using the advanced case-based reasoning and stochastic approaches, Applied Energy 150(15 July): 308–322. https://doi.org/10.1016/j.apenergy.2015.04.036
Hong, T.; Koo, C.; Kim, H. 2012c. A decision support model for improving a multi-family housing complex based on CO2 emission from electricity consumption, Journal of Environmental Management 112: 67–78. https://doi.org/10.1016/j.jenvman.2012.06.046
Hong, T.; Koo, C.; Kim, H.; Park, H. S. 2014c. Decision support model for establishing the optimal energy retrofit strategy for existing multi-family housing complexes, Energy Policy 66: 157–169. https://doi.org/10.1016/j.enpol.2013.10.057
Hong, T.; Koo, C.; Kwak, T.; Park, H.S. 2014b. An economic and environmental assessment for selecting the optimum new renewable energy system for educational facility, Renewable and Sustainable Energy Reviews 29: 286–300. https://doi.org/10.1016/j.rser.2013.08.061
Hong, T.; Koo, C.; Lee, M. 2013. Estimating the loss ratio of solar photovoltaic electricity generation through stochastic analysis, Journal of Construction Engineering and Project Management 3(3): 23–34. http://www.jcepm.org/english/viewtopic.php?t=437
Hong, T.; Koo, C.; Lee, S. 2014d. Benchmarks as a tool for free allocation through comparison with similar projects: focused on multi-family housing complex, Applied Energy 114: 663–675. https://doi.org/10.1016/j.apenergy.2013.10.035
Hong, T.; Koo, C.; Park, J.; Park, H. S. 2014a. A GIS (geographic information system)-based optimization model for estimating the electricity generation of the rooftop PV (photovoltaic) system, Energy 65: 190–199. https://doi.org/10.1016/j.energy.2013.11.082
Hong, T.; Koo, C.; Park, S. 2012b. A decision support model for improving a multi-family housing complex based on CO2 emission from gas energy consumption, Building and Environment 52: 142–151. https://doi.org/10.1016/j.buildenv.2012.01.001
Janjai, S. 2010. A method for estimating direct normal solar irradiation from satellite data for a tropical environment, Solar Energy 84(9): 1685–1695. https://doi.org/10.1016/j.solener.2010.05.017
Jeong, K.; Ji, C.; Koo, C.; Hong, T.; Park, H. S. 2015. A model for predicting the environmental impacts of educational facilities in the project planning phase, Journal of Cleaner Production 107: 538–549. https://doi.org/10.1016/j.jclepro.2014.01.027
Jeong, K.; Koo, C.; Hong, T.; Park, H. 2014. An estimation model for determining the annual energy cost budget in educational facilities using SARIMA (seasonal autoregressive integrated moving average) and ANN (artificial neural network), Energy 71: 71–79. https://doi.org/10.1016/j.energy.2014.04.027
Koo, C.; Hong, T. 2015a. A dynamic energy performance curve for evaluating the historical trends in the energy performance of existing buildings using a simplified case-based reasoning approach, Energy and Buildings 92: 338–350. https://doi.org/10.1016/j.enbuild.2015.02.004
Koo, C.; Hong, T. 2017. Development of a dynamic incentive and penalty program for improving the energy performance of existing buildings, Technological and Economic Development of Economy (in press).
Koo, C.; Hong, T.; Hyun, C. 2011. The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach, Expert Systems with Applications 38(7): 8597–8606. https://doi.org/10.1016/j.eswa.2011.01.063
Koo, C.; Hong, T.; Hyun, C.; Koo, K. 2010. A CBR-based hybrid model for predicting a construction duration and cost based on project characteristics in multi-family housing projects, Canadian Journal of Civil Engineering 37(5): 739–752. https://doi.org/10.1139/L10-007
Koo, C.; Hong, T.; Kim, J. 2014e. A decision support system for determining the optimal size of a new expressway service area: focused on the profitability, Decision Support Systems 67: 9–20. https://doi.org/10.1016/j.dss.2014.07.005
Koo, C.; Hong, T.; Kim, J.; Kim, H. 2015. An integrated multi-objective optimization model for establishing the low-carbon scenario 2020 to achieve the national carbon emissions reduction target for residential buildings, Renewable and Sustainable Energy Reviews 49: 410–425. https://doi.org/10.1016/j.rser.2015.04.120
Koo, C.; Hong, T.; Lee, M.; Park, H. S. 2013. Estimation of the monthly average daily solar radiation using geographic information system and advanced case-based reasoning, Environmental Science and Technology 47(9): 4829–4839. https://doi.org/10.1021/es303774a
Koo, C.; Hong, T.; Lee, M.; Park, H. S. 2014d. Development of a new energy efficiency rating system for existing residential buildings, Energy Policy 68: 218–231. https://doi.org/10.1016/j.enpol.2013.12.068
Koo, C.; Hong, T.; Park, H. S.; Yun, G. 2014b. Framework for the analysis of the potential of the rooftop photovoltaic system to achieve the net-zero energy solar buildings, Progress in Photovoltaics: Research and Applications 22(4): 462–478. https://doi.org/10.1002/pip.2448
Koo, C.; Kim, H.; Hong, T. 2014a. Framework for the analysis of the low-carbon scenario 2020 to achieve the national carbon Emissions reduction target: focused on educational facilities, Energy Policy 73: 356–367. https://doi.org/10.1016/j.enpol.2014.05.009
Koo, C.; Park, S.; Hong, T.; Park, H. S. 2014c. An estimation model for the heating and cooling demand of a residential building with a different envelope design using the finite element method, Applied Energy 115: 205–215. https://doi.org/10.1016/j.apenergy.2013.11.014
Korea Energy Management Corporation (KEMC). 2012. New and renewable energy white paper. Gyeonggi-do, South Korea, KEMC.
Lee, M.; Koo, C.; Hong, T.; Park, H. S. 2014a. Framework for the mapping of the monthly average daily solar radiation using an advanced case-based reasoning and a geostatistical technique, Environmen¬tal Science and Technology 48(8): 4604–4612. https://doi.org/10.1021/es405293u
Lee, S. W.; Lee, S. W.; Lee, S. Y.; Hong, W. H. 2014b. A study on estimation of the greenhouse gas emission from the road transportation infrastructure using the geostatistical analysis – a case of the Daegu, Journal of Korea Spatial Information Society 22(1): 9–17. https://doi.org/10.12672/ksis.2014.22.1.009
Lueken, C.; Cohen, G. E.; Apt, J. 2012. Costs of solar and wind power variability for reducing CO2 emissions, Environmental Science and Technology 46(17): 9761–9767. https://doi.org/10.1021/es204392a
Mohandes, M.; Rehman, S.; Halawani, T. O. 1998. Estimation of global solar radiation using artificial neural networks, Renewable Energy 14(1–4): 179–184. https://doi.org/10.1016/S0960-1481(98)00065-2
Mubiru, J.; Banda, E. J . K. B. 2008. Estimation of monthly average daily global solar irradiation using artificial neural networks, Solar Energy 82(2): 181–187. https://doi.org/10.1016/j.solener.2007.06.003
Polycarpou, A.C. 2006. Introduction to the finite element method in electromagnetics. U.S., Morgan & Claypool Publishers.
Ramedani, Z.; Omid, M.; Keyhani, A.; Shamshirband, S.; Khoshnevisan, B. 2014. Potential of radial basis function based support vector regression for global solar radiation prediction, Renewable and Sustainable Energy Reviews 39: 1005–1011. https://doi.org/10.1016/j.rser.2014.07.108
Reddy, K. S.; Ranjan, M. 2003. Solar resource estimation using artificial neural networks and compari¬son with other correlation models, Energy Conversion and Management 44(15): 2519–2530. https://doi.org/10.1016/S0196-8904(03)00009-8
Rehman, S.; Ghori, S. G. 2000. Spatial estimation of global solar radiation using geostatistics, Renewable Energy 21(3–4): 583–605. https://doi.org/10.1016/S0960-1481(00)00078-1
Saffaripour, M. H.; Mehrabian, M. A.; Bazargan, H. 2013. Predicting solar radiation fluxes for solar energy system applications, International Journal of Environmental Science and Technology 10(4): 761–768. https://doi.org/10.1007/s13762-013-0179-2
Şahin, M.; Kaya, Y.; Uyar, M. 2013. Comparison of ANN and MLR models for estimating solar radiation in Turkey using NOAA/AVHRR data, Advances in Space Research 51(5): 891–904. https://doi.org/10.1016/j.asr.2012.10.010
Seo, D.; Koo, C.; Hong, T. 2015. A Lagrangian finite element model for estimating the heating and cooling demand of a residential building with a different envelope design, Applied Energy 142: 66–79. https://doi.org/10.1016/j.apenergy.2014.12.051
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