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Machine Learning-Based Small Hydropower Potential Prediction under Climate Change

Author

Listed:
  • Jaewon Jung

    (Institute of Water Resources System, Inha University, Incheon 22201, Korea)

  • Heechan Han

    (Department of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO 80523, USA)

  • Kyunghun Kim

    (Department of Civil Engineering, Inha University, Incheon 22201, Korea)

  • Hung Soo Kim

    (Department of Civil Engineering, Inha University, Incheon 22201, Korea)

Abstract

As the effects of climate change are becoming severe, countries need to substantially reduce carbon emissions. Small hydropower (SHP) can be a useful renewable energy source with a high energy density for the reduction of carbon emission. Therefore, it is necessary to revitalize the development of SHP to expand the use of renewable energy. To efficiently plan and utilize this energy source, there is a need to assess the future SHP potential based on an accurate runoff prediction. In this study, the future SHP potential was predicted using a climate change scenario and an artificial neural network model. The runoff was simulated accurately, and the applicability of an artificial neural network to the runoff prediction was confirmed. The results showed that the total amount of SHP potential in the future will generally a decrease compared to the past. This result is applicable as base data for planning future energy supplies and carbon emission reductions.

Suggested Citation

  • Jaewon Jung & Heechan Han & Kyunghun Kim & Hung Soo Kim, 2021. "Machine Learning-Based Small Hydropower Potential Prediction under Climate Change," Energies, MDPI, vol. 14(12), pages 1-10, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3643-:d:577680
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    References listed on IDEAS

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    4. Olivier Cleynen & Dennis Powalla & Stefan Hoerner & Dominique Thévenin, 2022. "An Efficient Method for Computing the Power Potential of Bypass Hydropower Installations," Energies, MDPI, vol. 15(9), pages 1-13, April.

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