Abstract :

The Intelligent Energy Usage Monitoring System Powered By Support Vector Machine (SVM) Learning Aims To Provide An Efficient And Accurate Approach To Tracking And Analyzing Energy Consumption In Real Time. The System Collects Usage Data From Sensors Or Smart Meters And Processes It Through An SVM-based Model To Identify Patterns, Classify Consumption Behaviors, And Detect Unusual Or Excessive Usage. By Learning From Historical Data, The Model Can Predict Future Energy Demands And Support Informed Decision-making For Both Users And Service Providers. The System Presents Personalized Recommendations To Reduce Wastage, Optimize Appliance Usage, And Improve Overall Energy Efficiency. A User-friendly Interface Displays Detailed Consumption Statistics, Trend Analysis, And Alerts, Enabling Users To Take Timely Actions. The Proposed Solution Contributes To Sustainable Energy Management By Promoting Responsible Consumption And Reducing Operational Costs. Experimental Results Demonstrate That The SVM Model Provides High Accuracy In Classification And Prediction Compared To Traditional Methods, Making It A Reliable Tool For Smart Energy Monitoring Applications. Keywords: Intelligent Energy Monitoring, Support Vector Machine (SVM), Smart Grid, Energy Consumption Prediction, Real-time Analytics, IoT-based Monitoring, Power Usage Optimization, Demand-side Management, Anomaly Detection, Predictive Analytics.

Published:

11-12-2025

Issue:

Vol. 25 No. 12 (2025)

Page Nos:

169-174

Section:

Articles

License:

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

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