Multimodal Framework for Smart Building Occupancy Detection

Mohammed Awad Abuhussain, Badr Saad Alotaibi (), Yakubu Aminu Dodo, Ammar Maghrabi and Muhammad Saidu Aliero
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Mohammed Awad Abuhussain: Architectural Engineering Department, College of Engineering, Najran University, Najran 66426, Saudi Arabia
Badr Saad Alotaibi: Architectural Engineering Department, College of Engineering, Najran University, Najran 66426, Saudi Arabia
Yakubu Aminu Dodo: Architectural Engineering Department, College of Engineering, Najran University, Najran 66426, Saudi Arabia
Ammar Maghrabi: Urban and Engineering Research Department, The Custodian of the Two Holy Mosques Institute for Hajj and Umrah Research, Umm Al-Qura University, Makkah 24236, Saudi Arabia
Muhammad Saidu Aliero: Department of Information Technology, Kebbi State University Science and Technology, Aliero 863104, Nigeria

Sustainability, 2024, vol. 16, issue 10, 1-26

Abstract: Over the years, building appliances have become the major energy consumers to improve indoor air quality and occupants’ lifestyles. The primary energy usage in building sectors, particularly lighting, Heating, Ventilation, and Air conditioning (HVAC) equipment, is expected to double in the upcoming years due to inappropriate control operation activities. Recently, several researchers have provided an automated solution to turn HVAC and lighting on when the space is being occupied and off when the space becomes vacant. Previous studies indicate a lack of publicly accessible datasets for environmental sensing and suggest developing holistic models that detect buildings’ occupancy. Additionally, the reliability of their solutions tends to decrease as the occupancy grows in a building. Therefore, this study proposed a machine learning-based framework for smart building occupancy detection that considered the lighting parameter in addition to the HVAC parameter used in the existing studies. We employed a parametric classifier to ensure a strong correlation between the predicting parameters and the occupancy prediction model. This study uses a machine learning model that combines direct and environmental sensing techniques to obtain high-quality training data. The analysis of the experimental results shows high accuracy, precision, recall, and F1-score of the applied RF model (0.86, 0.99, 1.0, and 0.88 respectively) for occupancy prediction and substantial energy saving.

Keywords: Internet of Things; machine learning; occupancy prediction; energy saving (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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