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Compare several methods of select typical meteorological year for building energy simulation in China

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  • Li, Honglian
  • Yang, Yi
  • Lv, Kailin
  • Liu, Jing
  • Yang, Liu

Abstract

In the early architectural design and energy saving reconstruction of building, it is essential need the typical meteorological year（TMY）for building performance analysis. The accuracy of the typical meteorological year data is directly impact the energy simulation results. In order to obtain accurate TMY data for construction engineering applications, the paper does the following work: (1) Reviewed the progress of the typical meteorological year; (2) Several TMY generation methods were compared using the same raw weather data, discusses the applicability of these methods for the cities of typical climate zones in China. Based on the most recent and comprehensive first-hand ground observation data, this paper adopts three methods to generate TMY for the typical city covering the climate zoning of China. Result shows different TMY generation method has applicability in different region, and through the simulation of building energy consumption, the comprehensive method has obvious advantages. It shows that in a region with diverse regional climate characteristics such as China, one certain method for selecting TMY is not enough, and comprehensive methods should be proposed to meet the needs of different building types and energy consumption simulation.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:209:y:2020:i:c:s0360544220315735
    DOI: 10.1016/j.energy.2020.118465
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    References listed on IDEAS

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    Cited by:

    1. Yuan, Jihui & Huang, Pei & Chai, Jiale, 2022. "Development of a calibrated typical meteorological year weather file in system design of zero-energy building for performance improvements," Energy, Elsevier, vol. 259(C).
    2. Jiaxiang Lei & Honglian Li & Chengwang Li & Minrui Xu, 2023. "A New Method for Determining Outdoor Humidity Ratio of Natatorium in Transition Season," Energies, MDPI, vol. 16(7), pages 1-17, March.
    3. Li, Honglian & Zhang, Tiantian & Wang, An & Wang, Mengli & Huang, Jin & Hu, Yao, 2023. "A new method of generating extreme building energy year and its application," Energy, Elsevier, vol. 278(PB).
    4. Li, Honglian & Huang, Jin & Hu, Yao & Wang, Shangyu & Liu, Jing & Yang, Liu, 2021. "A new TMY generation method based on the entropy-based TOPSIS theory for different climatic zones in China," Energy, Elsevier, vol. 231(C).
    5. Li, Honglian & He, Xi & Hu, Yao & Lv, Wen & Yang, Liu, 2024. "Research on the generation method of missing hourly solar radiation data based on multiple neural network algorithm," Energy, Elsevier, vol. 287(C).
    6. Serena Summa & Giada Remia & Ambra Sebastianelli & Gianluca Coccia & Costanzo Di Perna, 2022. "Impact on Thermal Energy Needs Caused by the Use of Different Solar Irradiance Decomposition and Transposition Models: Application of EN ISO 52016-1 and EN ISO 52010-1 Standards for Five European Citi," Energies, MDPI, vol. 15(23), pages 1-18, November.
    7. Remizov, Alexey & Memon, Shazim Ali & Kim, Jong R., 2024. "Novel building energy performance-based climate zoning enhanced with spatial constraint," Applied Energy, Elsevier, vol. 355(C).
    8. Ciprian Cristea & Maria Cristea & Dan Doru Micu & Andrei Ceclan & Radu-Adrian Tîrnovan & Florica Mioara Șerban, 2022. "Tridimensional Sustainability and Feasibility Assessment of Grid-Connected Solar Photovoltaic Systems Applied for the Technical University of Cluj-Napoca," Sustainability, MDPI, vol. 14(17), pages 1-23, August.
    9. Icaro Figueiredo Vilasboas & Julio Augusto Mendes da Silva & Osvaldo José Venturini, 2023. "On the Summarization of Meteorological Data for Solar Thermal Power Generation Forecast," Energies, MDPI, vol. 16(7), pages 1-10, April.
    10. Jahns, Christopher & Osinski, Paul & Weber, Christoph, 2023. "A statistical approach to modeling the variability between years in renewable infeed on energy system level," Energy, Elsevier, vol. 263(PA).

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