AI-Driven Predictive Analytics for Energy Consumption Optimization in Smart Grids
Abstract
Efficient energy management is a cornerstone of sustainable smart grid systems. This paper presents an AI-driven predictive analytics framework for optimizing energy consumption in smart grids. The framework uses time-series forecasting models and reinforcement learning algorithms to predict energy demand and dynamically adjust supply. Experiments conducted on smart grid datasets demonstrate the system's ability to reduce energy wastage and balance load distribution effectively. The results underscore the potential of AI to improve energy efficiency, reduce costs, and support the integration of renewable energy sources in smart grid infrastructures.
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