AI-Based Smart Microgrid Energy Management System

The AI-Based Smart Microgrid Energy Management System aims to improve energy utilization and power management using IoT and machine learning. The system integrates solar power, battery storage, and transformer supply, with an ESP32 monitoring electrical and environmental parameters. MQTT enables real-time data communication, while a web dashboard supports remote monitoring and load control. Relay-based switching manages power sources according to predefined operating conditions. An LCD displays system status, and a Python-based backend stores data and generates alerts. Machine learning is proposed for estimating battery and transformer health to support predictive maintenance. The system provides a cost-effective approach to smart energy monitoring, automated power management, and reliable microgrid operation

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Publication Details

Journal
International Journal of Advanced Research in Science Communication and Technology
Published
2026-10-27
DOI
https://doi.org/10.48175/ijarsct-40325
Primary Topic
Microgrid Control and Optimization
Type
article
Field-Weighted Citation Impact
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article

AI-Based Smart Microgrid Energy Management System

Rahane Sayali Babasaheb Prof. Manisha landge, Rathod Kunal Shrirang, Dhomase Ajit Tanaji Pansare Atul Devidas
1 links
International Journal of Advanced Research in Science Communication and Technology
Microgrid Control and Optimization
article

AI-Based Smart Microgrid Energy Management System

Rahane Sayali Babasaheb Prof. Manisha landge, Rathod Kunal Shrirang, Dhomase Ajit Tanaji Pansare Atul Devidas
article en

Abstract

The AI-Based Smart Microgrid Energy Management System aims to improve energy utilization and power management using IoT and machine learning. The system integrates solar power, battery storage, and transformer supply, with an ESP32 monitoring electrical and environmental parameters. MQTT enables real-time data communication, while a web dashboard supports remote monitoring and load control. Relay-based switching manages power sources according to predefined operating conditions. An LCD displays system status, and a Python-based backend stores data and generates alerts. Machine learning is proposed for estimating battery and transformer health to support predictive maintenance. The system provides a cost-effective approach to smart energy monitoring, automated power management, and reliable microgrid operation

International Journal of Advanced Research in Science Communication and Technology
Amrutvahini College of Engineering (IN)
Industry, innovation and infrastructure, Affordable and clean energy
Openalex Percentile: Top 17%
Microgrid Control and Optimization
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