IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i19p6427-d651643.html
   My bibliography  Save this article

Solar Irradiation Evaluation through GIS Analysis Based on Grid Resolution and a Mathematical Model: A Case Study in Northeast Mexico

Author

Listed:
  • Fausto André Valenzuela-Domínguez

    (School of Engineering and Sciences, Tecnologico de Monterrey, Blvd. Enrique Mazón López 965, Hermosillo 83000, Sonora, Mexico)

  • Luis Alfonso Santa Cruz

    (School of Engineering and Sciences, Tecnologico de Monterrey, Blvd. Enrique Mazón López 965, Hermosillo 83000, Sonora, Mexico)

  • Enrique A. Enríquez-Velásquez

    (School of Engineering, The University of Edinburgh, Sanderson Building, Robert Stevenson Road, The King’s Buildings, Edinburgh EH9 3FB, UK)

  • Luis C. Félix-Herrán

    (School of Engineering and Sciences, Tecnologico de Monterrey, Blvd. Enrique Mazón López 965, Hermosillo 83000, Sonora, Mexico)

  • Victor H. Benitez

    (Department of Industrial Engineering, Universidad de Sonora, Hermosillo 83000, Sonora, Mexico)

  • Jorge de-J. Lozoya-Santos

    (School of Engineering and Sciences, Tecnologico de Monterrey, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Nuevo León, Mexico)

  • Ricardo A. Ramírez-Mendoza

    (School of Engineering and Sciences, Tecnologico de Monterrey, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Nuevo León, Mexico)

Abstract

The estimation of the solar resource on certain surfaces of the planet is a key factor in deciding where to establish solar energy collection systems. This research uses a mathematical model based on easy-access geographic and meteorological information to calculate total solar radiation at ground surface. This information is used to create a GIS analysis of the State of Nuevo León in Mexico and identify solar energy opportunities in the territory. The analyzed area was divided into a grid and the coordinates of each corner are used to feed the mathematical model. The obtained results were validated with statistical analyses and satellite-based estimations from the National Aeronautics and Space Administration (NASA). The applied approach and the results may be replicated to estimate solar radiation in other regions of the planet without requiring readings from on-site meteorological stations and therefore reducing the cost of decision-making regarding where to place the solar energy collection equipment.

Suggested Citation

  • Fausto André Valenzuela-Domínguez & Luis Alfonso Santa Cruz & Enrique A. Enríquez-Velásquez & Luis C. Félix-Herrán & Victor H. Benitez & Jorge de-J. Lozoya-Santos & Ricardo A. Ramírez-Mendoza, 2021. "Solar Irradiation Evaluation through GIS Analysis Based on Grid Resolution and a Mathematical Model: A Case Study in Northeast Mexico," Energies, MDPI, vol. 14(19), pages 1-37, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6427-:d:651643
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/19/6427/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/19/6427/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bala Bhavya Kausika & Wilfried G. J. H. M. van Sark, 2021. "Calibration and Validation of ArcGIS Solar Radiation Tool for Photovoltaic Potential Determination in the Netherlands," Energies, MDPI, vol. 14(7), pages 1-16, March.
    2. Saioa Etxebarria Berrizbeitia & Eulalia Jadraque Gago & Tariq Muneer, 2020. "Empirical Models for the Estimation of Solar Sky-Diffuse Radiation. A Review and Experimental Analysis," Energies, MDPI, vol. 13(3), pages 1-23, February.
    3. Heng, Yan & Lu, Chao-Lin & Yu, Luqing & Gao, Zhifeng, 2020. "The heterogeneous preferences for solar energy policies among US households," Energy Policy, Elsevier, vol. 137(C).
    4. Cheng, C.-L. & Shalabh, & Garg, G., 2014. "Coefficient of determination for multiple measurement error models," Journal of Multivariate Analysis, Elsevier, vol. 126(C), pages 137-152.
    5. Chen, Ji-Long & He, Lei & Yang, Hong & Ma, Maohua & Chen, Qiao & Wu, Sheng-Jun & Xiao, Zuo-lin, 2019. "Empirical models for estimating monthly global solar radiation: A most comprehensive review and comparative case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 91-111.
    6. Luis Sarmiento & Thorsten Burandt & Konstantin Löffler & Pao-Yu Oei, 2019. "Analyzing Scenarios for the Integration of Renewable Energy Sources in the Mexican Energy System—An Application of the Global Energy System Model (GENeSYS-MOD)," Energies, MDPI, vol. 12(17), pages 1-24, August.
    7. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Zeng, Wenzhi & Wang, Xiukang & Zou, Haiyang, 2019. "Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 186-212.
    8. Francesco Mancini & Benedetto Nastasi, 2020. "Solar Energy Data Analytics: PV Deployment and Land Use," Energies, MDPI, vol. 13(2), pages 1-18, January.
    9. Besharat, Fariba & Dehghan, Ali A. & Faghih, Ahmad R., 2013. "Empirical models for estimating global solar radiation: A review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 798-821.
    10. Enrique A. Enríquez-Velásquez & Victor H. Benitez & Sergey G. Obukhov & Luis C. Félix-Herrán & Jorge de-J. Lozoya-Santos, 2020. "Estimation of Solar Resource Based on Meteorological and Geographical Data: Sonora State in Northwestern Territory of Mexico as Case Study," Energies, MDPI, vol. 13(24), pages 1-41, 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. Zhao, Shuting & Wu, Lifeng & Xiang, Youzhen & Dong, Jianhua & Li, Zhen & Liu, Xiaoqiang & Tang, Zijun & Wang, Han & Wang, Xin & An, Jiaqi & Zhang, Fucang & Li, Zhijun, 2022. "Coupling meteorological stations data and satellite data for prediction of global solar radiation with machine learning models," Renewable Energy, Elsevier, vol. 198(C), pages 1049-1064.

    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. Guillermo Quiroga-Ocaña & Julio C. Montaño-Moreno & Enrique A. Enríquez-Velásquez & Victor H. Benitez & Luis C. Félix-Herrán & Jorge de-J. Lozoya-Santos & Ricardo A. Ramírez-Mendoza, 2021. "Computing and Assessment of Discrete Angle Positions for Optimizing the Solar Energy Harvesting for Urban Sustainable Development," Energies, MDPI, vol. 14(20), pages 1-19, October.
    2. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    3. Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    4. Hengtian Wang & Xiaolong Yang & Xinxin Xu & Liu Fei, 2021. "Exploring Opportunities and Challenges of Solar PV Power under Carbon Peak Scenario in China: A PEST Analysis," Energies, MDPI, vol. 14(11), pages 1-28, May.
    5. Qiu, Rangjian & Li, Longan & Wu, Lifeng & Agathokleous, Evgenios & Liu, Chunwei & Zhang, Baozhong & Luo, Yufeng & Sun, Shanlei, 2022. "Modeling daily global solar radiation using only temperature data: Past, development, and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    6. Qin, Shujing & Liu, Zhihe & Qiu, Rangjian & Luo, Yufeng & Wu, Jingwei & Zhang, Baozhong & Wu, Lifeng & Agathokleous, Evgenios, 2023. "Short–term global solar radiation forecasting based on an improved method for sunshine duration prediction and public weather forecasts," Applied Energy, Elsevier, vol. 343(C).
    7. Prieto, Jesús-Ignacio & García, David, 2022. "Global solar radiation models: A critical review from the point of view of homogeneity and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    8. Zang, Haixiang & Jiang, Xin & Cheng, LiLin & Zhang, Fengchun & Wei, Zhinong & Sun, Guoqiang, 2022. "Combined empirical and machine learning modeling method for estimation of daily global solar radiation for general meteorological observation stations," Renewable Energy, Elsevier, vol. 195(C), pages 795-808.
    9. Wu, Wei & Tang, Xiaoping & Lv, Jiake & Yang, Chao & Liu, Hongbin, 2021. "Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas," Renewable Energy, Elsevier, vol. 177(C), pages 148-163.
    10. Zhigao Zhou & Aiwen Lin & Lijie He & Lunche Wang, 2022. "Evaluation of Various Tree-Based Ensemble Models for Estimating Solar Energy Resource Potential in Different Climatic Zones of China," Energies, MDPI, vol. 15(9), pages 1-23, May.
    11. Makade, Rahul G. & Chakrabarti, Siddharth & Jamil, Basharat & Sakhale, C.N., 2020. "Estimation of global solar radiation for the tropical wet climatic region of India: A theory of experimentation approach," Renewable Energy, Elsevier, vol. 146(C), pages 2044-2059.
    12. Nabavi-Pelesaraei, Ashkan & Azadi, Hossein & Van Passel, Steven & Saber, Zahra & Hosseini-Fashami, Fatemeh & Mostashari-Rad, Fatemeh & Ghasemi-Mobtaker, Hassan, 2021. "Prospects of solar systems in production chain of sunflower oil using cold press method with concentrating energy and life cycle assessment," Energy, Elsevier, vol. 223(C).
    13. Mecibah, Mohamed Salah & Boukelia, Taqiy Eddine & Tahtah, Reda & Gairaa, Kacem, 2014. "Introducing the best model for estimation the monthly mean daily global solar radiation on a horizontal surface (Case study: Algeria)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 36(C), pages 194-202.
    14. Abajian, Alexander & Pretnar, Nick, 2021. "An Aggregate Perspective on the Geo-spatial Distribution of Residential Solar Panels," MPRA Paper 105481, University Library of Munich, Germany.
    15. Oei, Pao-Yu & Burandt, Thorsten & Hainsch, Karlo & Löffler, Konstantin & Kemfert, Claudia, 2020. "Lessons from Modeling 100% Renewable Scenarios Using GENeSYS-MOD," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 9(1), pages 103-120.
    16. Jamil, Basharat & Akhtar, Naiem, 2017. "Comparison of empirical models to estimate monthly mean diffuse solar radiation from measured data: Case study for humid-subtropical climatic region of India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1326-1342.
    17. Liu, Yanfeng & Zhou, Yong & Chen, Yaowen & Wang, Dengjia & Wang, Yingying & Zhu, Ying, 2020. "Comparison of support vector machine and copula-based nonlinear quantile regression for estimating the daily diffuse solar radiation: A case study in China," Renewable Energy, Elsevier, vol. 146(C), pages 1101-1112.
    18. Rohani, Abbas & Taki, Morteza & Abdollahpour, Masoumeh, 2018. "A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I)," Renewable Energy, Elsevier, vol. 115(C), pages 411-422.
    19. Smrutiranjan Nayak & Sanjeeb Kumar Kar & Subhransu Sekhar Dash & Pradeep Vishnuram & Sudhakar Babu Thanikanti & Benedetto Nastasi, 2022. "Enhanced Salp Swarm Algorithm for Multimodal Optimization and Fuzzy Based Grid Frequency Controller Design," Energies, MDPI, vol. 15(9), pages 1-22, April.
    20. Meenal, R. & Selvakumar, A. Immanuel, 2018. "Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters," Renewable Energy, Elsevier, vol. 121(C), pages 324-343.

    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:14:y:2021:i:19:p:6427-:d:651643. 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.