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Generation of typical meteorological year for different climates of China

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  • Jiang, Yingni

Abstract

Accurate prediction of building energy performance requires precise information of the local climate. Typical weather year files like typical meteorological year (TMY) are commonly used in building simulation. They are also essential for numerical analysis of sustainable and renewable energy systems. The present paper presents the generation of typical meteorological year (TMY) for eight typical cities representing the major climate zones of China. The data set, which includes global solar radiation data and other meteorological parameters referring to dry bulb temperature, relative humidity, wind speed, has been analyzed. The typical meteorological year is generated from the available meteorological data recorded during the period 1995–2004, using the Finkelstein-Schafer statistical method. The cumulative distribution function (CDF) for each year is compared with the CDF for the long-term composite of all the years in the period. Typical months for each of the 12 calendar months from the period of years are selected by choosing the one with the smallest deviation from the long-term CDF. The 12 typical months selected from the different years are used for the formulation of a TMY.

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  • Jiang, Yingni, 2010. "Generation of typical meteorological year for different climates of China," Energy, Elsevier, vol. 35(5), pages 1946-1953.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:5:p:1946-1953
    DOI: 10.1016/j.energy.2010.01.009
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    1. Skeiker, Kamal & Ghani, Bashar Abdul, 2009. "A software tool for the creation of a typical meteorological year," Renewable Energy, Elsevier, vol. 34(3), pages 544-554.
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    2. Zang, Haixiang & Xu, Qingshan & Bian, Haihong, 2012. "Generation of typical solar radiation data for different climates of China," Energy, Elsevier, vol. 38(1), pages 236-248.
    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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    8. 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).
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    13. S. Mirasgedis & E. Georgopoulou & Y. Sarafidis & K. Papagiannaki & D. P. Lalas, 2014. "The Impact of Climate Change on the Pattern of Demand for Bottled Water and Non‐Alcoholic Beverages," Business Strategy and the Environment, Wiley Blackwell, vol. 23(4), pages 272-288, May.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. Moazzen, Nazanin & Ashrafian, Touraj & Yilmaz, Zerrin & Karagüler, Mustafa Erkan, 2020. "A multi-criteria approach to affordable energy-efficient retrofit of primary school buildings," Applied Energy, Elsevier, vol. 268(C).
    19. Zhang, Wenhao & Li, Honglian & Wang, Mengli & Lv, Wen & Huang, Jin & Yang, Liu, 2024. "Enhancing typical Meteorological Year generation for diverse energy systems: A hybrid Sandia-machine learning approach," Renewable Energy, Elsevier, vol. 225(C).
    20. Li, Chong & Zhou, Dequn & Zheng, Yuan, 2018. "Techno-economic comparative study of grid-connected PV power systems in five climate zones, China," Energy, Elsevier, vol. 165(PB), pages 1352-1369.
    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.
    22. Carra, Elena & Ballestrín, Jesús & Polo, Jesús & Barbero, Javier & Fernández-Reche, Jesús, 2018. "Atmospheric extinction levels of solar radiation at Plataforma Solar de Almería. Application to solar thermal electric plants," Energy, Elsevier, vol. 145(C), pages 400-407.

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