INTELLIGENT DECISION-MAKING FRAMEWORK FOR AI-ENABLED DISTRIBUTION NETWORKS

The rapid transformation of electricity distribution networks has introduced significant operational complexity due to fluctuating demand, distributed energy resources, intermittent renewable generation, aging assets, and tighter power-quality expectations. Existing control methods may be inadequate for such environments because they typically depend on static network representations and predetermined operating rules. This study presents an AI-enabled decision-support architecture for the dynamic management of modern distribution networks. The proposed architecture integrates data obtained from smart meters, field sensors, distributed generation units, meteorological services, and supervisory control systems. A data-processing layer cleans and synchronizes these heterogeneous inputs, while predictive models assess short-term electricity demand, renewable power availability, equipment condition, network congestion, and fault probability. The predicted conditions are then combined with grid constraints and operational objectives through an intelligent optimization layer. This layer recommends or initiates actions related to demand-side management, load balancing, distributed resource dispatch, fault recovery, voltage control, and network reconfiguration. Feedback from actual network performance is continuously incorporated to refine the forecasting models and improve subsequent operational decisions. The framework is assessed using indicators such as prediction accuracy, decision latency, power-loss reduction, voltage stability, supply reliability, and computational efficiency. The findings show that the proposed architecture enables faster responses, more efficient resource allocation, and improved resilience compared with conventional distribution-management practices. Overall, the study provides a scalable approach for applying predictive intelligence and adaptive optimization to the development of reliable, autonomous, and sustainable electricity distribution networks.