Deep Learning and Adaptive Boosting for Hydroelectric Power Prediction Using Hydro-Meteorological Data: Insights and Feature Importance Analysis

Abstract

This study explores the use of modern artificial intelligence (AI) models such as Adaptive Boosting (AdaBoost), Autoencoder Based Regression Model (Autoencoder), Deep Neural Network (DNN), Echo State Network (ESN), Light Gradient Boosting Machine (LGBM) Residual Neural Network (ResNet) and feature importance techniques to predict energy production. Daily rainfall, streamflow, temperature and energy production data between 2016 and 2019 were used in the study. According to the feature importance analysis performed using the LGBM model, streamflow, temperature, and precipitation were identified as the most critical parameters influencing energy production. This structured AI-driven approach provided robust model evaluation and reliable performance measurements. As revealed by the analysis, the LGBM model showed the most accurate result with the values of root mean square error (RMSE: 98.27), mean absolute error (MAE: 73.93), Akaike information criterion (AIC: 17,100), Nash-Sutcliffe efficiency coefficient (NSE: 0.69), Kling-Gupta efficiency (KGE: 0.77), coefficient of determination (R2: 0.69), mean bias error (MBE: -5.23), bias factor (BF: 1.02), and percent bias (PBIAS: 1.51), while the DNN model came in with the second-best results. These findings emphasize both the effectiveness of deep learning and ensemble AI models and their practical application in improving hydroelectric power generation forecasting. Furthermore, the study underscores the importance of accurately monitoring and managing streamflow for the optimization of hydroelectric systems.