AI for Optimizing Energy Consumption in Smart Grids Using Predictive Analytics
Abstract
Efficient energy management is a key challenge in modern smart grids. This paper presents an AI-driven approach for optimizing energy consumption in smart grids using predictive analytics. The system uses machine learning models to forecast energy demand, identify consumption patterns, and recommend strategies for reducing waste. Real-time data from smart meters and IoT devices are integrated to provide accurate predictions and dynamic adjustments. Case studies demonstrate the system's effectiveness in reducing energy consumption and operational costs while maintaining grid stability. This research emphasizes the role of AI in promoting sustainable energy practices.
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