IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i2p301-d306184.html
   My bibliography  Save this article

Estimation and Mapping of Solar Irradiance for Korea by Using COMS MI Satellite Images and an Artificial Neural Network Model

Author

Listed:
  • YoungHyun Koo

    (Department of Geological Sciences, University of Texas at San Antonio, San Antonio, TX 78249, USA)

  • Myeongchan Oh

    (Department of Energy Systems Engineering, Seoul National University, Seoul 08826, Korea)

  • Sung-Min Kim

    (Department of Energy Engineering, Kangwon National University, Samcheok Campus, Samcheok 25913, Korea)

  • Hyeong-Dong Park

    (Department of Energy Systems Engineering, Seoul National University, Seoul 08826, Korea
    Research Institute of Energy and Resources, Seoul National University, Seoul 08826, Korea)

Abstract

The power capacity of solar photovoltaics (PVs) in Korea has grown dramatically in recent years, and an accurate estimation of solar resources is crucial for the efficient management of these solar PV systems. Since the number of solar irradiance measurement sites is insufficient for Korea, satellite images can be useful sources for estimating solar irradiance over a wide area of Korea. In this study, an artificial neural network (ANN) model was constructed to calculate hourly global horizontal solar irradiance (GHI) from Korea Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) images. Solar position variables and five COMS MI channels were used as inputs for the ANN model. The basic ANN model was determined to have a window size of five for the input satellite images and two hidden layers, with 30 nodes on each hidden layer. After these ANN parameters were determined, the temporal and spatial applicability of the ANN model for solar irradiance mapping was validated. The final ANN ensemble model, which calculated the hourly GHI from 10 independent ANN models, exhibited a correlation coefficient (R) of 0.975 and root mean square error (RMSE) of 54.44 W/m² (12.93%), which were better results than for other remote-sensing based works for Korea. Finally, GHI maps for Korea were generated using the final ANN ensemble model. This COMS-based ANN model can contribute to the efficient estimation of solar resources and the improvement of the operational efficiency of solar PV systems for Korea.

Suggested Citation

  • YoungHyun Koo & Myeongchan Oh & Sung-Min Kim & Hyeong-Dong Park, 2020. "Estimation and Mapping of Solar Irradiance for Korea by Using COMS MI Satellite Images and an Artificial Neural Network Model," Energies, MDPI, vol. 13(2), pages 1-19, January.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:2:p:301-:d:306184
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/2/301/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/2/301/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alonso-Montesinos, J. & Batlles, F.J., 2015. "The use of a sky camera for solar radiation estimation based on digital image processing," Energy, Elsevier, vol. 90(P1), pages 377-386.
    2. Yang, Dazhi & Gu, Chaojun & Dong, Zibo & Jirutitijaroen, Panida & Chen, Nan & Walsh, Wilfred M., 2013. "Solar irradiance forecasting using spatial-temporal covariance structures and time-forward kriging," Renewable Energy, Elsevier, vol. 60(C), pages 235-245.
    3. Linares-Rodriguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vazquez, David & Tovar-Pescador, Joaquin, 2013. "An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images," Energy, Elsevier, vol. 61(C), pages 636-645.
    4. Bikhtiyar Ameen & Heiko Balzter & Claire Jarvis & James Wheeler, 2019. "Modelling Hourly Global Horizontal Irradiance from Satellite-Derived Datasets and Climate Variables as New Inputs with Artificial Neural Networks," Energies, MDPI, vol. 12(1), pages 1-28, January.
    5. Global Energy Assessment Writing Team,, 2012. "Global Energy Assessment," Cambridge Books, Cambridge University Press, number 9780521182935, January.
    6. Fei Wang & Zengqiang Mi & Shi Su & Hongshan Zhao, 2012. "Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters," Energies, MDPI, vol. 5(5), pages 1-16, May.
    7. Diane Palmer & Ian Cole & Tom Betts & Ralph Gottschalg, 2017. "Interpolating and Estimating Horizontal Diffuse Solar Irradiation to Provide UK-Wide Coverage: Selection of the Best Performing Models," Energies, MDPI, vol. 10(2), pages 1-23, February.
    8. Linares-Rodríguez, Alvaro & Ruiz-Arias, José Antonio & Pozo-Vázquez, David & Tovar-Pescador, Joaquín, 2011. "Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks," Energy, Elsevier, vol. 36(8), pages 5356-5365.
    9. Global Energy Assessment Writing Team,, 2012. "Global Energy Assessment," Cambridge Books, Cambridge University Press, number 9781107005198, January.
    10. Oh, Myeongchan & Park, Hyeong-Dong, 2018. "A new algorithm using a pyramid dataset for calculating shadowing in solar potential mapping," Renewable Energy, Elsevier, vol. 126(C), pages 465-474.
    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. Ren, Simiao & Hu, Wayne & Bradbury, Kyle & Harrison-Atlas, Dylan & Malaguzzi Valeri, Laura & Murray, Brian & Malof, Jordan M., 2022. "Automated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis," Applied Energy, Elsevier, vol. 326(C).
    2. Elvina Faustina Dhata & Chang Ki Kim & Hyun-Goo Kim & Boyoung Kim & Myeongchan Oh, 2022. "Site-Adaptation for Correcting Satellite-Derived Solar Irradiance: Performance Comparison between Various Regressive and Distribution Mapping Techniques for Application in Daejeon, South Korea," Energies, MDPI, vol. 15(23), pages 1-20, November.

    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. Linares-Rodriguez, Alvaro & Quesada-Ruiz, Samuel & Pozo-Vazquez, David & Tovar-Pescador, Joaquin, 2015. "An evolutionary artificial neural network ensemble model for estimating hourly direct normal irradiances from meteosat imagery," Energy, Elsevier, vol. 91(C), pages 264-273.
    2. Wang, Lunche & Gong, Wei & Hu, Bo & Feng, Lan & Lin, Aiwen & Zhang, Ming, 2014. "Long-term variations of ultraviolet radiation in China from measurements and model reconstructions," Energy, Elsevier, vol. 78(C), pages 928-938.
    3. Germán Ramos Ruiz & Carlos Fernández Bandera, 2017. "Validation of Calibrated Energy Models: Common Errors," Energies, MDPI, vol. 10(10), pages 1-19, October.
    4. Tilmann Rave, 2013. "Innovationsindikatoren zum globalen Klimaschutz – FuE-Ausgaben und Patente," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 66(15), pages 34-41, August.
    5. Daniel Moran & Richard Wood, 2014. "Convergence Between The Eora, Wiod, Exiobase, And Openeu'S Consumption-Based Carbon Accounts," Economic Systems Research, Taylor & Francis Journals, vol. 26(3), pages 245-261, September.
    6. Lykke E. Andersen & Luis Carlos Jemio, 2016. "Decentralization and poverty reduction in Bolivia: Challenges and opportunities," Development Research Working Paper Series 01/2016, Institute for Advanced Development Studies.
    7. Tom Mikunda & Tom Kober & Heleen de Coninck & Morgan Bazilian & Hilke R�sler & Bob van der Zwaan, 2014. "Designing policy for deployment of CCS in industry," Climate Policy, Taylor & Francis Journals, vol. 14(5), pages 665-676, September.
    8. Jun Nakatani & Tamon Maruyama & Kosuke Fukuchi & Yuichi Moriguchi, 2015. "A Practical Approach to Screening Potential Environmental Hotspots of Different Impact Categories in Supply Chains," Sustainability, MDPI, vol. 7(9), pages 1-15, August.
    9. Fichter, Tobias & Soria, Rafael & Szklo, Alexandre & Schaeffer, Roberto & Lucena, Andre F.P., 2017. "Assessing the potential role of concentrated solar power (CSP) for the northeast power system of Brazil using a detailed power system model," Energy, Elsevier, vol. 121(C), pages 695-715.
    10. Selosse, Sandrine & Ricci, Olivia & Maïzi, Nadia, 2013. "Fukushima's impact on the European power sector: The key role of CCS technologies," Energy Economics, Elsevier, vol. 39(C), pages 305-312.
    11. Kamjoo, Azadeh & Maheri, Alireza & Putrus, Ghanim A., 2014. "Chance constrained programming using non-Gaussian joint distribution function in design of standalone hybrid renewable energy systems," Energy, Elsevier, vol. 66(C), pages 677-688.
    12. Mokri, Alaeddine & Aal Ali, Mona & Emziane, Mahieddine, 2013. "Solar energy in the United Arab Emirates: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 340-375.
    13. Perrihan Al-Riffai & Julian Blohmke & Clemens Breisinger & Manfred Wiebelt, 2015. "Harnessing the Sun and Wind for Economic Development? An Economy-Wide Assessment for Egypt," Sustainability, MDPI, vol. 7(6), pages 1-27, June.
    14. Kim, Yeong Jae & Wilson, Charlie, 2019. "Analysing energy innovation portfolios from a systemic perspective," Energy Policy, Elsevier, vol. 134(C).
    15. Sofia Dahlgren & Jonas Ammenberg, 2021. "Sustainability Assessment of Public Transport, Part II—Applying a Multi-Criteria Assessment Method to Compare Different Bus Technologies," Sustainability, MDPI, vol. 13(3), pages 1-30, January.
    16. Jan K. Kazak & Joanna A. Kamińska & Rafał Madej & Marta Bochenkiewicz, 2020. "Where Renewable Energy Sources Funds are Invested? Spatial Analysis of Energy Production Potential and Public Support," Energies, MDPI, vol. 13(21), pages 1-26, October.
    17. David Bryngelsson & Fredrik Hedenus & Daniel J. A. Johansson & Christian Azar & Stefan Wirsenius, 2017. "How Do Dietary Choices Influence the Energy-System Cost of Stabilizing the Climate?," Energies, MDPI, vol. 10(2), pages 1-13, February.
    18. Jin-Young Kim & Hyun-Goo Kim & Yong-Heack Kang, 2017. "Offshore Wind Speed Forecasting: The Correlation between Satellite-Observed Monthly Sea Surface Temperature and Wind Speed over the Seas around the Korean Peninsula," Energies, MDPI, vol. 10(7), pages 1-15, July.
    19. Frame, Damien & Hannon, Matthew & Bell, Keith & McArthur, Stephen, 2018. "Innovation in regulated electricity distribution networks: A review of the effectiveness of Great Britain's Low Carbon Networks Fund," Energy Policy, Elsevier, vol. 118(C), pages 121-132.
    20. Silva Herran, Diego & Dai, Hancheng & Fujimori, Shinichiro & Masui, Toshihiko, 2016. "Global assessment of onshore wind power resources considering the distance to urban areas," Energy Policy, Elsevier, vol. 91(C), pages 75-86.

    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:13:y:2020:i:2:p:301-:d:306184. 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.