Machine Learning-Based Small Hydropower Potential Prediction under Climate Change
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- Geoffrey Gasore & Arthur Santos & Etienne Ntagwirumugara & Daniel Zimmerle, 2023. "Sizing of Small Hydropower Plants for Highly Variable Flows in Tropical Run-of-River Installations: A Case Study of the Sebeya River," Energies, MDPI, vol. 16(3), pages 1-14, January.
- Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
- Jongsung Kim & Myungjin Lee & Heechan Han & Donghyun Kim & Yunghye Bae & Hung Soo Kim, 2022. "Case Study: Development of the CNN Model Considering Teleconnection for Spatial Downscaling of Precipitation in a Climate Change Scenario," Sustainability, MDPI, vol. 14(8), pages 1-20, April.
- 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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Keywords
artificial neural network; climate change; hydropower potential; small hydropower;All these keywords.
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