14 citations as recorded by crossref.

1. Impact of climatic factors on the prediction of hydroelectric power generation: a deep CNN-SVR approach M. Özbay Karakuş https://doi.org/10.1080/10106049.2023.2253203
2. Stochastic Simulation-Optimisation Framework for the Design and Assessment of Renewable Energy Systems Under Uncertainty G. Sakki et al. https://doi.org/10.2139/ssrn.4109850
3. Smart hydropower management: utilizing machine learning and deep learning method to enhance dam’s energy generation efficiency M. Sahin & M. Ozbay Karakus https://doi.org/10.1007/s00521-024-09613-1
4. Application of Machine Learning in Water Resources Management: A Systematic Literature Review F. Ghobadi & D. Kang https://doi.org/10.3390/w15040620
5. Short-Term Hydropower Generation Forecasting for Operational Planning and Early Energy Procurement: Multi-Model Evidence from Kazakhstan A. Rakhimzhanova et al. https://doi.org/10.3390/en19112520
6. Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources A. Krechowicz et al. https://doi.org/10.3390/en15239146
7. Forecasting Electricity Production in a Small Hydropower Plant (SHP) Using Artificial Intelligence (AI) D. Maciejewski et al. https://doi.org/10.3390/en17246401
8. Small hydropower plant enhanced with minimal storage capacity: Investigation of its techno-economic feasibility with intra-day regulation K. Drakaki et al. https://doi.org/10.1016/j.renene.2025.124601
9. A Weak-Coupling Flow-Power Forecasting Method for Small Hydropower Station Group B. Chen et al. https://doi.org/10.1155/2023/1214269
10. Parabolic Modeling Forecasts of Space and Time European Hydropower Production C. Lincaru et al. https://doi.org/10.3390/pr12061098
11. Prediction of hydroelectric power generation with machine learning and innovative combined deep learning techniques B. Yılmaz et al. https://doi.org/10.1007/s00477-025-03140-8
12. Design and Simulations of RT Na-S Battery/Supercapacitor Energy Storage Systems Integrated in Grid/Microgrid with Renewables H. Adeoye et al. https://doi.org/10.3390/batteries11110409
13. Stochastic simulation-optimization framework for the design and assessment of renewable energy systems under uncertainty G. Sakki et al. https://doi.org/10.1016/j.rser.2022.112886
14. An explainable deep learning approach for hydropower production forecasting: Evidence from 116 plants in China P. Du et al. https://doi.org/10.1016/j.energy.2026.141237