Digital Twin and Machine Learning Approaches for Renewable Energy System Optimization

This research introduced a combined digital twin and machine learning framework aimed at enhancing biofuel conversion efficiency and energy return on investment (EROI), while concurrently simulating battery degradation dynamics for sustainable energy systems. Six forecasting models were evaluated using leave-one-out cross-validation (LOOCV). In terms of conversion efficiency, linear regression delivered the highest performance (MAE = 1.22, RMSE = 1.49, R² = 0.84), whereas gradient boosting slightly enhanced predictive consistency in predicted–actual visualizations. In the case of EROI, linear regression consistently surpassed other methods (MAE = 0.21, RMSE = 0.26, R² = 0.89), demonstrating a 20–25% decrease in error relative to tree-based models. Analysis of feature importance showed that fermentation time (+ 0.47) and ethanol yield (+ 0.44) were the key predictors for conversion efficiency and EROI, whereas energy input (–0.56) had the most significant negative impact. Comparisons between actual and predicted outcomes from five experimental batches revealed average deviations of ± 1.1% for efficiency and ± 0.21 for EROI, demonstrating robust model generalization. The optimization phase of the digital twin utilized reinforcement learning alongside AutoML frameworks. Bayesian optimization provided the best results with yield increases of 28.1 L, an 18.2% boost in EROI, and a 12.2% reduction in energy consumption, reaching convergence within just 200 epochs. In contrast, deep Q-learning reached a reduced yield (26.3 L) and demonstrated a slower convergence rate (500 epochs). Complementary battery degradation modeling showed that capacity retention dropped to 82% after 300 cycles, accompanied by thermal variations reaching 45°C during discharge, highlighting the necessity for combined electrochemical-thermodynamic monitoring. In summary, the suggested framework resulted in significant improvements in yield (+ 15–18%), EROI (+ 12–18%), and energy savings (8–12%), while offering a scalable solution for sustainable bioenergy and battery management systems.