Online Monitoring and Short-Term Power Forecasting for Solar-Hydro Hybrid Energy Systems Using Hedge-BPNNT Dual Feedback

Solar–hydro hybrid energy systems combine rapidly varying photovoltaic output with slower hydraulic dynamics, necessitating source-aware online adaptation. This study evaluates a software-in-the-loop monitoring and forecasting framework using a compact Hedge-adaptive backpropagation neural network with separate solar and hydro feedback gains. The dataset comprises 3,843 mixed-source observations collected from 18 July 2026 at 22:35 to 17 August 2026 at 00:13. Irradiance and ambient temperature were obtained from external weather-model outputs, rainfall was retrieved from a public service when available, hydraulic states were simulated, and hybrid power was constructed as a reference rather than obtained from independently metered plant output. Feature engineering generated 3,823 model-ready targets, which were evaluated using a chronological 60/20/20 development–validation–test split, with scaling fitted exclusively on the development set. On 764 common final-test targets, dual feedback reduced the mean absolute percentage error (MAPE) from 6.80 ± 3.04% for the matched no-feedback Hedge-BPNNT to 2.47 ± 0.57% across five prespecified random seeds. However, support vector regression (SVR) achieved the lowest one-step MAPE of 0.96% and the lowest MAPE at all 24 continuity-safe forecast lead times in the four-hour comparison. For the largest 10% of ten-minute power changes, dual feedback outperformed persistence, long short-term memory (LSTM), and the no-feedback variant, but remained inferior to SVR and Random Forest. The mean computation time for dual feedback was 0.111 ms per one-step prediction and update and 3.409 ms per 24-step recursive forecast. These findings indicate that source-specific feedback provides a useful adaptation mechanism within a matched neural-network architecture, while SVR delivers the strongest overall forecasting accuracy on the constructed-reference dataset.