Hybrid Optimized Machine Learning Models for Forecasting Electricity Demand and Generation: A Comparative Analysis - Jiang, Liandian; Huang, Pengfei; Tian, Junyang; Li, Haiyong; Liu, Bin; Liang, Shan; Wang, Jian (Informatica, 2025)
Increasingly complicated and volatile electricity demand considerably complicates the job of operators and planners of energy systems. For enhancing grid reliability and efficiency in resource allocation, realistic forecasting is thus in high demand regarding overall electricity demand and peak generation capacity. The study will introduce a new approach by adopting ML techniques, namely CatBoost and support vector regression, for predicting electricity demand and maximum generation capacity. Optimization algorithms for metaheuristics, such as phasor particle swarm optimizer and chaos game optimizer, are used to improve model performance. To this end, four hybrid models were developed: namely, CatBoost-CGO, CatBoost-PPSO, SVR-CGO, and SVR-PPSO. The performance was evaluated using statistical indices such as MAE, RMSE, MAPE, and R². Among the models, CatBoost-PPSO demonstrated the highest accuracy with R² = 0.955 for total demand and R² = 0.958 for maximum generation in the test set, outperforming other combinations. These results validate the effectiveness of hybrid optimization in improving prediction accuracy.