Intelligent Home Automation Framework Using Sensor Fusion and Machine Learning for Energy Efficiency and Thermal Comfort

Okorodudu O. Franklin, Gracious Omede, Etinosa Osawe

Abstract


This paper presents an innovative, intelligent home automation framework integrating sensor fusion and machine learning to promote energy efficiency and thermal comfort in residential settings. Utilising low-cost hardware such as the Arduino Uno R3, Passive Infrared (PIR) sensors, KY-018 photoresistors, and KY-028 temperature sensors, the system achieves a human presence detection accuracy of 95.3% via a Random Forest classifier. Experimental validation over three months across multiple households demonstrates a high system reliability of 99.7%, a response time of 1.2 seconds, and an 85% cost reduction relative to commercial alternatives. This research lays the groundwork for sustainable smart homes by providing a mathematical model for optimizing energy use and a Unified Modeling Language (UML) model of the system architecture.   These results highlight the importance of open-source technology that is both inexpensive and has the potential to promote smart building systems globally.

Keywords


Intelligent Home Automation Sensor Fusion Machine Learning Energy Efficiency Thermal Comfort Arduino Random Forest Adaptive Control

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References


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DOI: http://doi.org/10.11591/ijict.v15i2.pp545-552

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