Daily Power Generation Forecasting Method for a Group of Small Hydropower Stations Considering the Spatial and Temporal Distribution of Precipitation—South China Case Study
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- Gang Li & Bao-Jian Li & Xu-Guang Yu & Chun-Tian Cheng, 2015. "Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants," Energies, MDPI, vol. 8(10), pages 1-14, October.
- Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
- Kong, Yigang & Wang, Jie & Kong, Zhigang & Song, Furong & Liu, Zhiqi & Wei, Congmei, 2015. "Small hydropower in China: The survey and sustainable future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 425-433.
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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.
- Eric Stefan Miele & Nicole Ludwig & Alessandro Corsini, 2023. "Multi-Horizon Wind Power Forecasting Using Multi-Modal Spatio-Temporal Neural Networks," Energies, MDPI, vol. 16(8), pages 1-15, April.
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Keywords
small hydropower stations; daily power generation forecasting; temporal and spatial distribution of precipitation; multimodal deep learning;All these keywords.
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