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A Hybrid Method for Generation of Typical Meteorological Years for Different Climates of China

Author

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  • Haixiang Zang

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Miaomiao Wang

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Jing Huang

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Zhinong Wei

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Guoqiang Sun

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

Abstract

Since a representative dataset of the climatological features of a location is important for calculations relating to many fields, such as solar energy system, agriculture, meteorology and architecture, there is a need to investigate the methodology for generating a typical meteorological year (TMY). In this paper, a hybrid method with mixed treatment of selected results from the Danish method, the Festa-Ratto method, and the modified typical meteorological year method is proposed to determine typical meteorological years for 35 locations in six different climatic zones of China (Tropical Zone, Subtropical Zone, Warm Temperate Zone, Mid Temperate Zone, Cold Temperate Zone and Tibetan Plateau Zone). Measured weather data (air dry-bulb temperature, air relative humidity, wind speed, pressure, sunshine duration and global solar radiation), which cover the period of 1994–2015, are obtained and applied in the process of forming TMY. The TMY data and typical solar radiation data are investigated and analyzed in this study. It is found that the results of the hybrid method have better performance in terms of the long-term average measured data during the year than the other investigated methods. Moreover, the Gaussian process regression (GPR) model is recommended to forecast the monthly mean solar radiation using the last 22 years (1994–2015) of measured data.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:12:p:1094-:d:85774
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    References listed on IDEAS

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    Cited by:

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    2. 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.
    3. Hyun-Jin Lee & Shin-Young Kim & Chang-Yeol Yun, 2017. "Comparison of Solar Radiation Models to Estimate Direct Normal Irradiance for Korea," Energies, MDPI, vol. 10(5), pages 1-12, April.
    4. 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).
    5. Yassaghi, Hamed & Gurian, Patrick L. & Hoque, Simi, 2020. "Propagating downscaled future weather file uncertainties into building energy use," Applied Energy, Elsevier, vol. 278(C).
    6. 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.
    7. Giovanni Pernigotto & Alessandro Prada & Francesca Cappelletti & Andrea Gasparella, 2017. "Impact of Reference Years on the Outcome of Multi-Objective Optimization for Building Energy Refurbishment," Energies, MDPI, vol. 10(11), pages 1-23, November.
    8. 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.

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