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On the Summarization of Meteorological Data for Solar Thermal Power Generation Forecast

Author

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  • Icaro Figueiredo Vilasboas

    (Industrial Engineering Program (PEI), Federal University of Bahia (UFBA), Salvador 40210-630, BA, Brazil)

  • Julio Augusto Mendes da Silva

    (Industrial Engineering Program (PEI), Federal University of Bahia (UFBA), Salvador 40210-630, BA, Brazil)

  • Osvaldo José Venturini

    (Excellence Group in Thermal Power and Distributed Generation (NEST), Federal University of Itajubá (UNIFEI), Itajubá 37500-005, MG, Brazil)

Abstract

The establishment of the typical weather conditions of a given locality is of fundamental importance to determine the optimal configurations for solar thermal power plants and to calculate feasibility indicators in the power plant design phase. Therefore, this work proposes a summarization method to statistically represent historical weather data using typical meteorological days (TMDs) based on the cumulative distribution function (CDF) and hourly normalized root mean square difference (nRMSD). The proposed approach is compared with regular Sandia selection in forecasting the electricity produced by a solar thermal power plant in ten different Brazilian cities. Considering the determination of the annual generation of electricity, the results obtained show that when considering an overall average of weather characteristics, commonly used for analyzing solar thermal power plant designs, the normalized mean average error (nMAE) is 20.8 ± 4.8% relative to the use of historical data of 20 years established at hourly intervals. On the other hand, a typical meteorological year (TMY) is the most accurate approach (nMAE = 1.0 ± 1.1%), but the costliest in computational time (CT = 381.6 ± 56.3 s). Some TMD cases, in turn, present a reasonable trade-off between computational time and accuracy. The case using 4 TMD, for example, increased the error by about 11 percentual points while the computational time was reduced by about 81 times, which is quite significant for the simulation and optimization of complex heliothermic systems.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:3297-:d:1117760
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    References listed on IDEAS

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    1. 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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    3. Rana, Mashud & Sethuvenkatraman, Subbu & Heidari, Rahmat & Hands, Stuart, 2022. "Solar thermal generation forecast via deep learning and application to buildings cooling system control," Renewable Energy, Elsevier, vol. 196(C), pages 694-706.
    4. Farges, O. & Bézian, J.J. & El Hafi, M., 2018. "Global optimization of solar power tower systems using a Monte Carlo algorithm: Application to a redesign of the PS10 solar thermal power plant," Renewable Energy, Elsevier, vol. 119(C), pages 345-353.
    5. 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.
    6. 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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    1. Igor V. Ilin & Oksana Yu. Iliashenko & Egor M. Schenikov, 2023. "An Approach to Forecasting the Structure of Energy Generation in the Age of Energy Transition Based on the Automated Determination of Factor Significance," Energies, MDPI, vol. 17(1), pages 1-18, December.

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