Smart Energy Optimization Using Occupancy Detection and Real-Time Monitoring

Abstract

Energy wastage in educational institutions is a major issue caused by unnecessary operation of lighting and ventilation systems in unoccupied spaces. Continuous energy loss increases electricity cost and indirectly raises carbon emissions associated with power generation. This paper presents an IoT-based Smart Campus Carbon Footprint Management system for intelligent monitoring, automated control, and energy awareness in campus environments. The proposed system uses an ESP32 microcontroller as the central controller with occupancy sensing, energy measurement, relay-based load control, and cloud connectivity. A Passive Infrared sensor detects room occupancy and automatically switches electrical loads according to user presence. Electrical parameters are monitored in real time and uploaded to a cloud dashboard for visualization and supervision. Energy consumption data is further used to estimate carbon footprint, enabling users to understand environmental impact. The integrated system improves energy efficiency, reduces manual dependency, lowers operational cost, and supports sustainable campus infrastructure. Prototype-level evaluation shows reliable occupancy detection, stable remote monitoring, and measurable reduction in unnecessary energy usage.

Introduction

The proposed Smart Campus Carbon Footprint Management system aims to reduce energy wastage in educational institutions through intelligent automation and real-time monitoring. Traditional manual control methods often leave lights and fans running in empty classrooms, increasing electricity costs and carbon emissions. To address this issue, the system integrates IoT technologies, occupancy detection, energy monitoring, cloud connectivity, and carbon footprint estimation into a low-cost, scalable platform suitable for classrooms and indoor spaces.

The system uses a PIR sensor to detect room occupancy and an ESP32 microcontroller to process sensor data, control appliances, and manage Wi-Fi communication. When motion is detected, electrical loads such as lights and fans are turned ON; if no occupancy is detected for a specified time, the system automatically switches them OFF to prevent energy wastage. An energy sensing unit continuously measures voltage, current, power, and total energy consumption, while the collected data is uploaded to a cloud dashboard for remote monitoring and analysis. The system also estimates carbon emissions using energy consumption data and emission factors, helping administrators evaluate environmental impact.

The operational workflow includes occupancy detection, power measurement, automatic load control, cloud data transmission, and real-time energy and carbon emission updates. Hardware components include the ESP32 microcontroller, PIR sensor, energy sensor, relay module, and regulated power supply. Communication with the cloud is achieved through the MQTT protocol, enabling continuous monitoring and control.

Experimental results demonstrated effective occupancy-based automation, reliable energy monitoring, and stable cloud communication. The system achieved approximately 20–30% energy savings compared to conventional manual operation while providing real-time visibility of energy usage through the Blynk platform. Major advantages include reduced electricity consumption, lower operational costs, automated appliance control, carbon footprint awareness, and scalability for larger campus deployments.

Future enhancements may include machine learning for occupancy prediction and demand forecasting, integration with renewable energy systems and smart meters, and additional environmental sensors such as temperature, air quality, and daylight sensors for more advanced energy optimization.

Conclusion

This paper presented an IoT-based Smart Campus Carbon Footprint Management system for intelligent energy conservation in educational environments. By integrating occupancy sensing, automated load control, cloud monitoring, and carbon emission estimation, the proposed solution addresses both operational efficiency and sustainability goals. The system is economical, scalable, and suitable for real-world deployment in classrooms, offices, and institutional buildings.

References

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Copyright

Copyright © 2026 Krishna V U, Asif Ali, Nishal K A, Habeeba R V, Praveen K P. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.