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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
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    References listed on IDEAS

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    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.

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