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Comparison of methods for generating typical meteorological year using meteorological data from a tropical environment

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  • Janjai, S.
  • Deeyai, P.

Abstract

This paper presents the comparison of methods for generating typical meteorological year (TMY) data set using a 10-year period of meteorological data from four stations in a tropical environment of Thailand. These methods are the Sadia National Laboratory method, the Danish method and the Festa and Ratto method. In investigating their performance, these methods were employed to generate TMYs for each station. For all parameters of the TMYs and the stations, statistical test indicates that there is no significant difference between the 10-year average values of these parameters and the corresponding average values from TMY generated from each method. The TMY obtained from each method was also used as input data to simulate two solar water heating systems and two photovoltaic systems with different sizes at the four stations by using the TRNSYS simulation program. Solar fractions and electrical output calculated using TMYs are in good agreement with those computed employing the 10-year period hourly meteorological data. It is concluded that the performance of the three methods has no significant difference for all stations under this investigation. Due to its simplicity, the method of Sandia National Laboratories is recommended for the generation of TMY for this tropical environment. The TMYs developed in this work can be used for solar energy and energy conservation applications at the four locations in Thailand.

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  • Janjai, S. & Deeyai, P., 2009. "Comparison of methods for generating typical meteorological year using meteorological data from a tropical environment," Applied Energy, Elsevier, vol. 86(4), pages 528-537, April.
    • Handle: RePEc:eee:appene:v:86:y:2009:i:4:p:528-537
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    Cited by:

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    3. Vincenzo Costanzo & Gianpiero Evola & Marco Infantone & Luigi Marletta, 2020. "Updated Typical Weather Years for the Energy Simulation of Buildings in Mediterranean Climate. A Case Study for Sicily," Energies, MDPI, vol. 13(16), pages 1-24, August.
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    6. Li, Honglian & Yang, Yi & Lv, Kailin & Liu, Jing & Yang, Liu, 2020. "Compare several methods of select typical meteorological year for building energy simulation in China," Energy, Elsevier, vol. 209(C).
    7. Pusat, Saban & Ekmekçi, İsmail & Akkoyunlu, Mustafa Tahir, 2015. "Generation of typical meteorological year for different climates of Turkey," Renewable Energy, Elsevier, vol. 75(C), pages 144-151.
    8. Oluwaseu Kilanko & Sunday O Oyedepo & Joseph O Dirisu & Richard O Leramo & Philip Babalola & Abraham K Aworinde & Mfon Udo & Alexander M Okonkwo & Marvelous I Akomolafe, 2023. "Typical meteorological year data analysis for optimal usage of energy systems at six selected locations in Nigeria," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 18, pages 637-658.
    9. Chan, A.L.S., 2016. "Generation of typical meteorological years using genetic algorithm for different energy systems," Renewable Energy, Elsevier, vol. 90(C), pages 1-13.
    10. Haixiang Zang & Miaomiao Wang & Jing Huang & Zhinong Wei & Guoqiang Sun, 2016. "A Hybrid Method for Generation of Typical Meteorological Years for Different Climates of China," Energies, MDPI, vol. 9(12), pages 1-19, December.
    11. Abreu, Edgar F.M. & Canhoto, Paulo & Prior, Victor & Melicio, R., 2018. "Solar resource assessment through long-term statistical analysis and typical data generation with different time resolutions using GHI measurements," Renewable Energy, Elsevier, vol. 127(C), pages 398-411.
    12. Xinying Fan & Bin Chen & Changfeng Fu & Lingyun Li, 2020. "Research on the Influence of Abrupt Climate Changes on the Analysis of Typical Meteorological Year in China," Energies, MDPI, vol. 13(24), pages 1-16, December.
    13. Meyers, Steven & Schmitt, Bastian & Vajen, Klaus, 2018. "Renewable process heat from solar thermal and photovoltaics: The development and application of a universal methodology to determine the more economical technology," Applied Energy, Elsevier, vol. 212(C), pages 1537-1552.
    14. Sun, Jingting & Li, Zhengrong & Xiao, Fu & Xiao, Jianzhuang, 2020. "Generation of typical meteorological year for integrated climate based daylight modeling and building energy simulation," Renewable Energy, Elsevier, vol. 160(C), pages 721-729.
    15. Ariadna Carrobé & Lídia Rincón & Ingrid Martorell, 2021. "Thermal Monitoring and Simulation of Earthen Buildings. A Review," Energies, MDPI, vol. 14(8), pages 1-47, April.
    16. Fan, Xinying, 2022. "A method for the generation of typical meteorological year data using ensemble empirical mode decomposition for different climates of China and performance comparison analysis," Energy, Elsevier, vol. 240(C).
    17. Kulesza, Kinga, 2017. "Comparison of typical meteorological year and multi-year time series of solar conditions for Belsk, central Poland," Renewable Energy, Elsevier, vol. 113(C), pages 1135-1140.
    18. Sosa-Tinoco, Ian & Peralta-Jaramillo, Juan & Otero-Casal, Carlos & López- Agüera, A. & Miguez-Macho, G. & Rodríguez-Cabo, I., 2016. "Validation of a global horizontal irradiation assessment from a numerical weather prediction model in the south of Sonora–Mexico," Renewable Energy, Elsevier, vol. 90(C), pages 105-113.
    19. Benghanem, M., 2009. "Measurement of meteorological data based on wireless data acquisition system monitoring," Applied Energy, Elsevier, vol. 86(12), pages 2651-2660, December.
    20. Jiang, Yingni, 2010. "Generation of typical meteorological year for different climates of China," Energy, Elsevier, vol. 35(5), pages 1946-1953.
    21. Tejero-González, Ana & Andrés-Chicote, Manuel & García-Ibáñez, Paola & Velasco-Gómez, Eloy & Rey-Martínez, Francisco Javier, 2016. "Assessing the applicability of passive cooling and heating techniques through climate factors: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 727-742.

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