Abstract :

The Increasing Complexity Of Modern Energy Systems, Driven By Growing Demand And The Integration Of Renewable Sources, Necessitates Intelligent Management Solutions. Traditional Approaches Often Lack Real-time Decision-making And Optimization Capabilities. This Paper Proposes An AI-based Energy Management System (EMS) That Integrates Machine Learning For Demand Forecasting, Optimization Models For Resource Distribution, And Reinforcement Learning For Adaptive Control. By Leveraging Real-time Data From Smart Meters And IoT Devices, The System Dynamically Adjusts Operations Based On Consumption Patterns And External Conditions. Testing Within A Simulated Smart Grid Environment Demonstrates Enhanced Energy Efficiency, Peak Load Reduction, And Improved Renewable Energy Integration, Underscoring The System’s Potential To Advance Sustainable And Intelligent Energy Infrastructures. Keywords– Artificial Intelligence, Smart Energy Management, Machine Learning, Demand Forecasting, Renewable Energy Integration, Energy Optimization, Sustainable Energy Systems.

Published:

25-9-2025

Issue:

Vol. 25 No. 9 (2025)

Page Nos:

436-440

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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