AI Powered ESP32 Energy Management System

DOI

10.2991/978-94-6463-948-3_71How to use a DOI?

Keywords

Edge AI; Smart Microgrids; Battery Optimization; DOIT ESP32; Intelligent Monitoring; IoT Energy Systems

Abstract

The rapid spread of the application of renewable energy has created an acute need to create intelligent systems that will be able to optimize energy distribution, storage and use in real time. Traditional energy manage-ment systems tend to be inflexible, lack predictive capability and do not possess autonomous fault detection leading to inefficiencies and energy waste. In an attempt to mitigate these limitations, this paper will present an AI-based renewable energy management system possessing smart monitoring and display functions to be applied in commercial and defense applications. It features Hall effect instantaneous load sensors, INA21 battery condition sensors, and supports easy replacement of different Li-ion battery packs to maintain a continuous power supply. The short-range energy demand is predicted by an AI-based model on a DOIT ESP32 (30-pin) microcontroller, which improves the efficiency of allocation, and reduces wastage. A dashboard developed using Flask and Stream-lit provides a real-time visualization on the consumption, storage, and wastage data and system alerts using Wi-Fi. There is a further provision of reliability in the form of a fail-safe element that interrupts power flow and triggering alarms when irregular loads or battery anomalies or critical limits are detected. The system will lower costs, reduce wastage, and increase efficiency, help control loss, and sustain energy consumption in future renewable installations by combining low-cost sensing, lightweight AI prediction, and smart control.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jayendra S. Jadhav
AU  - Vedant Nigade
AU  - Pranjal Chavan
AU  - Sanyukta Pawar
AU  - Aashirwad Mehare
AU  - Aditya Whandhekar
PY  - 2026
DA  - 2026/01/06
TI  - AI Powered ESP32 Energy Management System
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 1041
EP  - 1053
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_71
DO  - 10.2991/978-94-6463-948-3_71
ID  - Jadhav2026
ER  -