Prediction of hydropower generation via machine learning algorithms at three Gorges Dam, China

• Main Authors: Sattar Hanoon M., Najah Ahmed A., Razzaq A., Oudah A.Y., Alkhayyat A., Feng Huang Y., kumar P., El-Shafie A.
• Other Authors: 57266877500
• Format: Article
• Published: Ain Shams University 2024
• Subjects: ARIMA; artificial neural network (ANN); hydropower generation (HPG); machine learning (ML); support vector machine (SVM); Decision making; Forecasting; Hydroelectric power; Learning algorithms; Neural networks; Reservoirs (water); Sensitivity analysis; Stochastic models; Stochastic systems; Uncertainty analysis; Artificial neural network; Auto-regressive; Autoregressive integrated moving aveage; Hydro-power generation; Hydropower generation; Machine learning; Machine learning algorithms; Machine-learning; Support vector machine; Support vectors machine; Support vector machines

Machine learning models have been effectively applied to predict certain variable in several engineering applications where the variable is highly stochastic in nature and complex to identify utilizing the classical mathematical models. Therefore, this study investigates the capability of various ma...