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Generation of typical meteorological years using genetic algorithm for different energy systems

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  • Chan, A.L.S.

Abstract

Computer simulation plays an important role in investigating the thermal/energy performance of buildings and energy systems. In order to reduce the computational time and provide a consistent form of weather data, simulation run with multi-year weather files is generally avoided. In contrast, representative weather data is widely adopted. For developing typical meteorological year (TMY) weather files, Sandia method is one of the commonly adopted approaches. During the generation of TMY, different weighting factors are assigned to some key climatic indices. Currently, the values of weighting factors mainly depend on the researchers' judgement. As these weighting factors can express the relative importance of impact of a particular climatic index on the thermal/energy performance of an energy system, computer simulation using different TMYs may lead to different conclusions. Therefore, it is inappropriate to apply one single TMY for all energy systems. In this study, a novel TMY weather file generator has been developed to link up an optimization algorithm and an energy simulation program. Through four application examples (one air-conditioned building and three renewable energy systems), this weather file generator demonstrated its capability to search optimal/near optimal combinations of weighting factors for generating appropriate TMY for computer simulations of different energy systems.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:90:y:2016:i:c:p:1-13
    DOI: 10.1016/j.renene.2015.12.052
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    References listed on IDEAS

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    1. Jiang, Yingni, 2010. "Generation of typical meteorological year for different climates of China," Energy, Elsevier, vol. 35(5), pages 1946-1953.
    2. 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.
    3. Ohunakin, Olayinka S. & Adaramola, Muyiwa S. & Oyewola, Olanrewaju M. & Fagbenle, Richard O., 2013. "Generation of a typical meteorological year for north–east, Nigeria," Applied Energy, Elsevier, vol. 112(C), pages 152-159.
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    7. 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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