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.
Highlights
Prediction under Climate Change.Hydropower is an important renewable energy source
The artificial neural network (ANN) model used in this study had a standard three-layer network
For the runoff prediction using the ANN model, the training period was from January 1995 to December 2015, the validation period was from January 2016 to December 2020, and the test period was from January 2021 to December 2030
Summary
A runoff was simulated using various hydrological models to evaluate the impacts of climate change on the hydropower potential [9,21,22,23,24,25]. An ANN model is based on the structure of neurons while taking into account nonlinearity and shows highly accurate results in complex systems with less impact from outliers Because of these strengths, ANNs have been widely used in hydrological and environmental models to study complex nonlinear processes such as the rainfall-runoff process. This study applied the ANN model to predict the future runoff and estimated the SHP potential based on the runoff prediction.
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