SMART CAST – AI Powered Load Forecasting for Smart Grids

Abstract

The increasing complexity and variability of electricity consumption in modern power systems have highlighted the need for intelligent, real-time forecasting solutions to ensure reliable, cost-efficient, and sustainable grid oper ations. This research presents Smart Cast, an AI-driven load forecasting frame work that integrates Long Short-Term Memory (LSTM) networks with Gradient Boosting models to capture both nonlinear temporal patterns and external influ encing factors such as weather dynamics. The system leverages historical smart meter data along with real-time sensor inputs to generate accurate short- and mid term demand predictions. In addition, Smart Cast incorporates anomaly detection techniques to proactively identify irregular consumption behaviors that may in dicate faults, energy theft, or operational inefficiencies. Outputs are delivered through an interactive web dashboard, enabling utility operators and industrial consumers to visualize demand behavior and receive cost-aware recommenda tions for optimized energy usage. By enhancing grid planning, reducing opera tional risks, and increasing renewable integration potential, Smart Cast presents a scalable and practical solution to advancing smart-grid intelligence and overall energy sustainability.