Indoor Energy Management and Power Consumption Analysis Using IOT Technology

Liew, Kian Keat (2026) Indoor Energy Management and Power Consumption Analysis Using IOT Technology. Final Year Project (Bachelor), Tunku Abdul Rahman University of Management and Technology.

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Abstract

The increasing demand for sustainable energy management in university buildings presents challenges such as high operational costs, lack of real-time monitoring, and difficulties in predicting dynamic consumption patterns. This project addresses these issues by developing an IoT-based indoor energy management system that integrates environmental and energy monitoring with advanced visualization techniques. The system employs IoT sensors to collect real-time data on parameters such as temperature, humidity, CO₂, VOC, lighting, and energy consumption. Communication is achieved through lightweight and interoperable protocols including MQTT, OPC UA, and Firebase, ensuring reliable data transmission and structured storage. A Node-RED dashboard was implemented to provide interactive monitoring and control of devices such as air conditioners, lighting, and door systems. A key feature of the system is the integration of a 3D digital twin model using the Speckle Viewer. This enables spatial visualization of real-time sensor data through interactive hotspots mapped to specific rooms. Users can switch between building-level overviews and detailed room-level views, enhancing situational awareness and decision-making. Implementation and testing confirmed successful data flow across MQTT, Firebase, and OPC UA platforms. The outputs, validated through MQTT Explorer, Firebase Realtime Database, and OPC UA logs, demonstrate the system’s capability for real-time data visualization and interactive control. While testing was based on simulated data, results indicate strong potential for reducing energy waste, optimizing power usage, and supporting sustainability initiatives in smart campus environments. Future deployment with physical sensors will provide further validation of robustness and scalability.