Abstract :

The Transition Toward Net-zero Residential Communities Has Become A Critical Objective In Addressing Global Energy Challenges, Reducing Carbon Emissions, And Promoting Sustainable Urban Development. This Paper Presents An Intelligent Energy Management Framework Designed To Optimize Energy Generation, Storage, And Consumption Within Residential Communities. The Proposed Framework Integrates Renewable Energy Sources Such As Solar Photovoltaic Systems, Battery Energy Storage Units, Smart Meters, And Internet Of Things (IoT)-enabled Devices To Create A Coordinated And Adaptive Energy Ecosystem. Advanced Data Analytics And Machine Learning Algorithms Are Employed To Forecast Energy Demand, Predict Renewable Energy Generation, And Dynamically Schedule Energy Resources To Maximize Efficiency While Minimizing Operational Costs And Carbon Emissions. The Framework Incorporates Real-time Monitoring And Automated Control Mechanisms That Enable Demand-side Management, Load Balancing, And Peak Demand Reduction. Through Intelligent Decision-making, Residential Energy Consumption Patterns Are Optimized Without Compromising User Comfort. The System Further Supports Bidirectional Energy Exchange With The Smart Grid, Facilitating Energy Trading And Enhancing Grid Stability. Performance Evaluation Demonstrates That The Proposed Framework Significantly Improves Renewable Energy Utilization, Reduces Dependency On Conventional Power Sources, Lowers Energy Costs, And Contributes Substantially Toward Achieving Netzero Energy Goals. The Results Indicate That The Integration Of Artificial Intelligence, IoT Technologies, And Distributed Energy Resources Can Provide A Scalable And Sustainable Solution For Future Smart Residential Communities.

Published:

15-11-2023

Issue:

Vol. 23 No. 11 (2023)

Page Nos:

279 - 288

Section:

Articles

License:

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

How to Cite