Ai-based Energy Management System

• Author(s):

  Mr Sathishkumar S | Saravanan M | Tonija Prabha S | Udhayasaran k | Vethatharshan R

• Keywords:

  Artificial Intelligence, Smart Energy Management, Energy Consumption Forecasting, Machine Learning Optimization, Load Scheduling, Real-Time Energy Monitoring, Demand Response Systems, Sustainable Energy Utilization, Intelligent Power Management

• Abstract:

  The Rapid Growth Of Energy Demand, Increasing Electricity Costs, And The Need For Sustainable Power Utilization Present Significant Challenges To Traditional Energy Management Practices. This Paper Presents An AI-Based Smart Energy Management System That Leverages Artificial Intelligence And Machine Learning Techniques To Enable Intelligent Monitoring, Forecasting, And Optimization Of Energy Consumption In Residential And Industrial Environments. The Proposed System Integrates Real-time Sensor Data, Historical Energy Usage Patterns, And Environmental Parameters To Analyze Consumption Behavior And Predict Future Energy Demand With High Accuracy The Architecture Incorporates A Multi-layered Intelligence Framework Offering Four Functional Modules. Module 1 Performs Real-time Energy Monitoring And Anomaly Detection Using Data-driven Analytics. Module 2 Applies Machine Learning–based Load Forecasting To Predict Peak Demand And Consumption Trends. Module 3 Enables Automated Energy Optimization Through AI-driven Decision-making, Dynamically Scheduling Loads To Minimize Energy Wastage And Operational Costs. Module 4 Provides Adaptive Control And User-centric Insights Through A Smart Dashboard, Ensuring Transparency And Actionable Recommendations. The System Architecture Comprises Three Integrated Components: An IoT-enabled Data Acquisition Layer For Continuous Energy Sensing, An AI-powered Analytics Engine For Prediction And Optimization, And A Cloud-based Management Platform For Visualization And Control. A Key Feature Of The Proposed Solution Is Its Autonomous Learning Capability, Which Continuously Refines Energy Optimization Strategies Based On Evolving Usage Patterns And Feedback. Experimental Evaluation Demonstrates That The System Effectively Reduces Peak Energy Consumption, Improves Energy Efficiency, And Enhances Decision-making Without Requiring Significant Infrastructure Modifications. The Results Confirm That AI-driven Smart Energy Management Is A Scalable, Cost-effective, And Sustainable Approach For Modern Power Systems, Offering A Practical Pathway Toward Intelligent And Energy-efficient Ecosystems

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Other Details

• Paper id:

  IJSARTV12I4104845

• Published in:

  Volume: 12 Issue: 4 April 2026

• Publication Date:

  2026-04-03

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