IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v103y2017icp70-80.html
   My bibliography  Save this article

A guideline to select an estimation model of daily global solar radiation between geostatistical interpolation and stochastic simulation approaches

Author

Listed:
  • Jeong, D.I.
  • St-Hilaire, A.
  • Gratton, Y.
  • Bélanger, C.
  • Saad, C.

Abstract

This study compares geostatistical interpolation and stochastic simulation approaches for the estimation of daily global solar radiation (GSR) on a horizontal surface in order to fill in missing values and to extend short record length of a meteorological station. A guideline to select an approach is suggested based on this comparison. Three geostatistical interpolation models are developed using the nearest neighbor (NN), inverse distance weighted (IDW), and ordinary kriging (OK) schemes. Three stochastic simulation models are also developed using the artificial neural network (ANN) method with daily temperature (ANN(T)), relative humidity (ANN(H)), and both (ANN(TH)) variables as predictors. The six models are compared at 13 meteorological stations located across southern Quebec, Canada. The three geostatistical interpolation models yield better performances at stations located in a high density area of GSR measuring stations compared to the three stochastic simulation models. The guideline suggests an optimal approach by comparing a threshold distance, estimated according to a performance criteria of a stochastic simulation model, to the distance between a target and its nearest neighboring station. Additionally, the spatial correlation strength of daily GSRs and the at-site correlation strength between daily GSRs and the predictor variables should be considered.

Suggested Citation

  • Jeong, D.I. & St-Hilaire, A. & Gratton, Y. & Bélanger, C. & Saad, C., 2017. "A guideline to select an estimation model of daily global solar radiation between geostatistical interpolation and stochastic simulation approaches," Renewable Energy, Elsevier, vol. 103(C), pages 70-80.
    • Handle: RePEc:eee:renene:v:103:y:2017:i:c:p:70-80
    DOI: 10.1016/j.renene.2016.11.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148116309855
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2016.11.022?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Behrang, M.A. & Assareh, E. & Noghrehabadi, A.R. & Ghanbarzadeh, A., 2011. "New sunshine-based models for predicting global solar radiation using PSO (particle swarm optimization) technique," Energy, Elsevier, vol. 36(5), pages 3036-3049.
    2. Jiang, Yingni, 2008. "Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models," Energy Policy, Elsevier, vol. 36(10), pages 3833-3837, October.
    3. Rehman, Shafiqur & Ghori, Saleem G, 2000. "Spatial estimation of global solar radiation using geostatistics," Renewable Energy, Elsevier, vol. 21(3), pages 583-605.
    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. Yeji Lee & Doosung Choi & Yongho Jung & Myeongjin Ko, 2022. "Application of Technology to Develop a Framework for Predicting Power Output of a PV System Based on a Spatial Interpolation Technique: A Case Study in South Korea," Energies, MDPI, vol. 15(22), pages 1-22, November.
    2. Zhu, Weiyu & Xu, Yuanming & Du, Huafei & Li, Jun, 2019. "Thermal performance of high-altitude solar powered scientific balloon," Renewable Energy, Elsevier, vol. 135(C), pages 1078-1096.
    3. Vásquez, Cristina & Célleri, Rolando & Córdova, Mario & Carrillo-Rojas, Galo, 2022. "Improving reference evapotranspiration (ETo) calculation under limited data conditions in the high Tropical Andes," Agricultural Water Management, Elsevier, vol. 262(C).
    4. Ceballos, Juan Carlos & Porfirio, Anthony Carlos Silva & Oricchio, Patricio Alberto & Posse, Gabriela, 2022. "Characterization of the annual regime of surface solar irradiance over Argentine Pampean Region using GL1.2 satellite-based data," Renewable Energy, Elsevier, vol. 194(C), pages 526-537.
    5. Nouri, Milad & Homaee, Mehdi, 2022. "Reference crop evapotranspiration for data-sparse regions using reanalysis products," Agricultural Water Management, Elsevier, vol. 262(C).

    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. Teke, Ahmet & Yıldırım, H. Başak & Çelik, Özgür, 2015. "Evaluation and performance comparison of different models for the estimation of solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1097-1107.
    2. Mostafavi, Elham Sadat & Ramiyani, Sara Saeidi & Sarvar, Rahim & Moud, Hashem Izadi & Mousavi, Seyyed Mohammad, 2013. "A hybrid computational approach to estimate solar global radiation: An empirical evidence from Iran," Energy, Elsevier, vol. 49(C), pages 204-210.
    3. Yadav, Amit Kumar & Malik, Hasmat & Chandel, S.S., 2015. "Application of rapid miner in ANN based prediction of solar radiation for assessment of solar energy resource potential of 76 sites in Northwestern India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1093-1106.
    4. Koo, Choongwan & Hong, Taehoon & Jeong, Kwangbok & Ban, Cheolwoo & Oh, Jeongyoon, 2017. "Development of the smart photovoltaic system blind and its impact on net-zero energy solar buildings using technical-economic-political analyses," Energy, Elsevier, vol. 124(C), pages 382-396.
    5. Li, Danny H.W. & Lou, Siwei, 2018. "Review of solar irradiance and daylight illuminance modeling and sky classification," Renewable Energy, Elsevier, vol. 126(C), pages 445-453.
    6. Işık, Erdem & Inallı, Mustafa, 2018. "Artificial neural networks and adaptive neuro-fuzzy inference systems approaches to forecast the meteorological data for HVAC: The case of cities for Turkey," Energy, Elsevier, vol. 154(C), pages 7-16.
    7. Sarmiento, Nilsa & Belmonte, Silvina & Dellicompagni, Pablo & Franco, Judith & Escalante, Karina & Sarmiento, Joaquín, 2019. "A solar irradiation GIS as decision support tool for the Province of Salta, Argentina," Renewable Energy, Elsevier, vol. 132(C), pages 68-80.
    8. Chu, Yinghao & Li, Mengying & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2022. "A network of sky imagers for spatial solar irradiance assessment," Renewable Energy, Elsevier, vol. 187(C), pages 1009-1019.
    9. Long, Huan & Zhang, Zijun & Su, Yan, 2014. "Analysis of daily solar power prediction with data-driven approaches," Applied Energy, Elsevier, vol. 126(C), pages 29-37.
    10. Bessafi, Miloud & Oree, Vishwamitra & Khoodaruth, Abdel & Chabriat, Jean-Pierre, 2020. "Impact of decomposition and kriging models on the solar irradiance downscaling accuracy in regions with complex topography," Renewable Energy, Elsevier, vol. 162(C), pages 1992-2003.
    11. Wen, Lei & Song, Qianqian, 2023. "ELCC-based capacity value estimation of combined wind - storage system using IPSO algorithm," Energy, Elsevier, vol. 263(PB).
    12. Sabzpooshani, Majid & Mohammadi, Kasra, 2014. "Establishing new empirical models for predicting monthly mean horizontal diffuse solar radiation in city of Isfahan, Iran," Energy, Elsevier, vol. 69(C), pages 571-577.
    13. Olatomiwa, Lanre & Mekhilef, Saad & Shamshirband, Shahaboddin & Petković, Dalibor, 2015. "Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1784-1791.
    14. Behrang, M.A. & Assareh, E. & Ghalambaz, M. & Assari, M.R. & Noghrehabadi, A.R., 2011. "Forecasting future oil demand in Iran using GSA (Gravitational Search Algorithm)," Energy, Elsevier, vol. 36(9), pages 5649-5654.
    15. Jabar H. Yousif & Hussein A. Kazem & John Boland, 2017. "Predictive Models for Photovoltaic Electricity Production in Hot Weather Conditions," Energies, MDPI, vol. 10(7), pages 1-19, July.
    16. Voyant, Cyril & Paoli, Christophe & Muselli, Marc & Nivet, Marie-Laure, 2013. "Multi-horizon solar radiation forecasting for Mediterranean locations using time series models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 44-52.
    17. Shamshirband, Shahaboddin & Mohammadi, Kasra & Yee, Por Lip & Petković, Dalibor & Mostafaeipour, Ali, 2015. "A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1031-1042.
    18. Notton, Gilles & Paoli, Christophe & Ivanova, Liliana & Vasileva, Siyana & Nivet, Marie Laure, 2013. "Neural network approach to estimate 10-min solar global irradiation values on tilted planes," Renewable Energy, Elsevier, vol. 50(C), pages 576-584.
    19. Dahmani, Kahina & Notton, Gilles & Voyant, Cyril & Dizene, Rabah & Nivet, Marie Laure & Paoli, Christophe & Tamas, Wani, 2016. "Multilayer Perceptron approach for estimating 5-min and hourly horizontal global irradiation from exogenous meteorological data in locations without solar measurements," Renewable Energy, Elsevier, vol. 90(C), pages 267-282.
    20. Notton, Gilles & Paoli, Christophe & Vasileva, Siyana & Nivet, Marie Laure & Canaletti, Jean-Louis & Cristofari, Christian, 2012. "Estimation of hourly global solar irradiation on tilted planes from horizontal one using artificial neural networks," Energy, Elsevier, vol. 39(1), pages 166-179.

    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:eee:renene:v:103:y:2017:i:c:p:70-80. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.