Design and evaluation of a hybrid forecasting system for electric power consumption using intelligent parameter tuning and nonlinear data modeling

Accurate prediction of electricity consumption is a complex, nonlinear task, and traditional linear, fixed-parameter models often fail to capture dynamic demand patterns. To overcome these challenges, this study proposes an innovative hybrid forecasting framework that integrates Support Vector Regression (SVR) with six advanced meta-heuristic optimization algorithms—Salp Swarm Algorithm (SSA), Ant Lion Optimizer (ALO), Satin Bowerbird Optimizer (SBO), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Grey Wolf Optimizer (GWO)—for intelligent hyperparameter tuning. This multi-algorithm optimization strategy enhances model adaptability and reduces dependence on a single heuristic. A K-fold cross-validation procedure was employed to ensure robustness and minimize overfitting, and a real-world electricity consumption dataset was used for evaluation using multiple statistical metrics. The results demonstrate that the proposed hybrid SVR models significantly improve forecasting accuracy compared with the baseline SVR, with SVR-SSA and SVR-ALO achieving the best performance. Specifically, the R 2 values improved by up to 9.97%, and the MAPE values decreased by over 97%, confirming the effectiveness of combining SVR with meta-heuristic optimization. However, this study is limited to a fixed set of meta-heuristic algorithms and a single regional dataset. Future research will focus on extending the framework to include newer optimization techniques (e.g., MRFO, HHO, SMA) and ensemble learning approaches such as CatBoost and LightGBM, as well as testing generalizability across multi-regional and multi-seasonal datasets. • Hybrid SVR models enhance electricity consumption forecasting accuracy. • Six meta-heuristic algorithms optimize SVR hyperparameters ( ɛ , γ , C). • K-Fold cross-validation ensures robust evaluation and avoids overfitting. • SVR-SSA and SVR-ALO models achieve the highest predictive performance. • SVR-SBO boosts R² by 9.97% and cuts MAPE by 97.62% in training.