Sustainability-Oriented Adaptive Load Balancing in Smart Grids Through Real-Time Electricity Demand Forecasting

Accurate electricity demand forecasting is essential for improving energy efficiency, operational resilience, and sustainability in modern smart grids. However, existing approaches often treat forecasting and operational control as separate tasks, limiting the effective use of predictive information for sustainable electricity management. This study proposes a forecasting-driven adaptive load-balancing framework that integrates short-term electricity demand forecasting with state-aware operational control within a unified decision-making architecture. A Gradient Boosting Regressor (GBR) predicts future electricity demand, while forecast estimates and uncertainty information guide an adaptive controller designed to improve load distribution during periods of high grid stress. The framework is evaluated using approximately 282,000 half-hourly operational data points from the National Grid Electricity System Operator, covering 2009–2025. On the National Grid ESO forecasting test set, the GBR achieves a mean absolute error of 260.10 MW, a root mean square error of 363.45 MW, a symmetric mean absolute percentage error of 1.079%, and an R2 of 0.9960. These forecasting metrics are calculated from the National Grid ESO demand series, while the IEEE 30-bus system is used exclusively for DC optimal power flow feasibility assessment. Operational evaluations indicate that the proposed adaptive approach reduces peak demand by 27.6%, decreases load variability by 53.22%, and increases the load factor from 0.5777 to 0.7540 compared with a conventional static strategy. Additional temporal validation, uncertainty analysis, scalability tests, sensitivity assessments, and ablation experiments provide further evidence of the framework’s robustness and computational practicality. By enabling demand-responsive load management, the proposed approach offers potential benefits for energy efficiency, grid resilience, and the integration of variable renewable energy resources. These outcomes support sustainable electricity system management, although improvements in renewable energy utilisation, emissions, and environmental performance require direct evaluation. The findings are based on offline historical evaluation; live grid trials, hardware-in-the-loop experiments, and closed-loop operational testing remain necessary before practical deployment can be established.