Machine Learning-Based Small Hydropower Potential Prediction under Climate Change

Jaewon Jung, Heechan Han, Kyunghun Kim and Hung Soo Kim
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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

Energies, 2021, vol. 14, issue 12, 1-10

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.

Keywords: artificial neural network; climate change; hydropower potential; small hydropower (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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