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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. 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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    3. 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.
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    4. 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.
    5. 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.
    6. 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.
    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.
    8. Hadidian Moghaddam, Mohammad Jafar & Kalam, Akhtar & Nowdeh, Saber Arabi & Ahmadi, Abdollah & Babanezhad, Manoochehr & Saha, Sajeeb, 2019. "Optimal sizing and energy management of stand-alone hybrid photovoltaic/wind system based on hydrogen storage considering LOEE and LOLE reliability indices using flower pollination algorithm," Renewable Energy, Elsevier, vol. 135(C), pages 1412-1434.
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    10. 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.
    11. García, Ignacio & Torres, José Luis, 2018. "Temporal downscaling of test reference years: Effects on the long-term evaluation of photovoltaic systems," Renewable Energy, Elsevier, vol. 122(C), pages 392-405.
    12. Germán Ramos Ruiz & Carlos Fernández Bandera, 2017. "Validation of Calibrated Energy Models: Common Errors," Energies, MDPI, vol. 10(10), pages 1-19, October.
    13. Ailliot, Pierre & Boutigny, Marie & Koutroulis, Eftichis & Malisovas, Athanasios & Monbet, Valérie, 2020. "Stochastic weather generator for the design and reliability evaluation of desalination systems with Renewable Energy Sources," Renewable Energy, Elsevier, vol. 158(C), pages 541-553.

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