IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i7p3297-d1117760.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/7/3297/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/7/3297/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    2. Chen, Chaoyi & Pinar, Mehmet & Stengos, Thanasis, 2020. "Renewable energy consumption and economic growth nexus: Evidence from a threshold model," Energy Policy, Elsevier, vol. 139(C).
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gerard Bikorimana & Charles Rutikanga & Didier Mwizerwa, 2020. "Linking energy consumption with economic growth: Rwanda as a case study," ECONOMICS AND POLICY OF ENERGY AND THE ENVIRONMENT, FrancoAngeli Editore, vol. 2020(2), pages 181-200.
    2. Abdul Rehman & Hengyun Ma & Magdalena Radulescu & Crenguta Ileana Sinisi & Zahid Yousaf, 2021. "Energy Crisis in Pakistan and Economic Progress: Decoupling the Impact of Coal Energy Consumption in Power and Brick Kilns," Mathematics, MDPI, vol. 9(17), pages 1-15, August.
    3. Mara Madaleno & Manuel Carlos Nogueira, 2023. "How Renewable Energy and CO 2 Emissions Contribute to Economic Growth, and Sustainability—An Extensive Analysis," Sustainability, MDPI, vol. 15(5), pages 1-15, February.
    4. Xu, Guangyue & Yang, Hualiu & Schwarz, Peter, 2022. "A strengthened relationship between electricity and economic growth in China: An empirical study with a structural equation model," Energy, Elsevier, vol. 241(C).
    5. Victor T. Ojapinwa & Dami Lawani, 2022. "Diaspora Remittances, Renewable Energy and Enterprise Growth in Nigeria," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 25(83), pages 57-73, June.
    6. Feng Dong & Yuling Pan, 2020. "Evolution of Renewable Energy in BRI Countries: A Combined Econometric and Decomposition Approach," IJERPH, MDPI, vol. 17(22), pages 1-18, November.
    7. Hugo O. Garcés & Claudia Durán & Eduardo Espinosa & Alejandro Jerez & Fredi Palominos & Marcela Hinojosa & Raúl Carrasco, 2022. "Monitoring of Thermal Comfort and Air Quality for Sustainable Energy Management inside Hospitals Based on Online Analytical Processing and the Internet of Things," IJERPH, MDPI, vol. 19(19), pages 1-23, September.
    8. Kamil Makieła & Błażej Mazur & Jakub Głowacki, 2022. "The Impact of Renewable Energy Supply on Economic Growth and Productivity," Energies, MDPI, vol. 15(13), pages 1-13, June.
    9. Abdul Rehman & Hengyun Ma & Magdalena Radulescu & Crenguta Ileana Sinisi & Loredana Maria Paunescu & MD Shabbir Alam & Rafael Alvarado, 2021. "The Energy Mix Dilemma and Environmental Sustainability: Interaction among Greenhouse Gas Emissions, Nuclear Energy, Urban Agglomeration, and Economic Growth," Energies, MDPI, vol. 14(22), pages 1-21, November.
    10. Hussain, C.M. Iftekhar & Duffy, Aidan & Norton, Brian, 2020. "Thermophotovoltaic systems for achieving high-solar-fraction hybrid solar-biomass power generation," Applied Energy, Elsevier, vol. 259(C).
    11. Liang Xie & Xianzhong Mu & Kuanyuting Lu & Dongou Hu & Guangwen Hu, 2023. "The time-varying relationship between CO2 emissions, heterogeneous energy consumption, and economic growth in China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(8), pages 7769-7793, August.
    12. Arrif, Toufik & Hassani, Samir & Guermoui, Mawloud & Sánchez-González, A. & A.Taylor, Robert & Belaid, Abdelfetah, 2022. "GA-GOA hybrid algorithm and comparative study of different metaheuristic population-based algorithms for solar tower heliostat field design," Renewable Energy, Elsevier, vol. 192(C), pages 745-758.
    13. Sultan Salem & Noman Arshed & Ahsan Anwar & Mubasher Iqbal & Nyla Sattar, 2021. "Renewable Energy Consumption and Carbon Emissions—Testing Nonlinearity for Highly Carbon Emitting Countries," Sustainability, MDPI, vol. 13(21), pages 1-17, October.
    14. Mariola Piłatowska & Andrzej Geise, 2021. "Impact of Clean Energy on CO 2 Emissions and Economic Growth within the Phases of Renewables Diffusion in Selected European Countries," Energies, MDPI, vol. 14(4), pages 1-24, February.
    15. Mehmet Balcilar & Ojonugwa Usman & George N. Ike, 2023. "Investing green for sustainable development without ditching economic growth," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(2), pages 728-743, April.
    16. Dogan, Eyup & Altinoz, Buket & Madaleno, Mara & Taskin, Dilvin, 2020. "The impact of renewable energy consumption to economic growth: A replication and extension of Inglesi-Lotz (2016)," Energy Economics, Elsevier, vol. 90(C).
    17. 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).
    18. 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.
    19. Miloš Žarković & Slobodan Lakić & Jasmina Ćetković & Bojan Pejović & Srdjan Redzepagic & Irena Vodenska & Radoje Vujadinović, 2022. "Effects of Renewable and Non-Renewable Energy Consumption, GHG, ICT on Sustainable Economic Growth: Evidence from Old and New EU Countries," Sustainability, MDPI, vol. 14(15), pages 1-27, August.
    20. Lee, Chi-Chuan & Zhang, Jian & Hou, Shanshuai, 2023. "The impact of regional renewable energy development on environmental sustainability in China," Resources Policy, Elsevier, vol. 80(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:7:p:3297-:d:1117760. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.