Non-Intrusive Room Occupancy Prediction Performance Analysis Using Different Machine Learning Techniques

Muhammad S. Aliero (), Muhammad F. Pasha, David T. Smith, Imran Ghani, Muhammad Asif, Seung Ryul Jeong and Moveh Samuel
Additional contact information
Muhammad S. Aliero: School of Information Technology, Monash University, Subang Jaya 47500, Malaysia
Muhammad F. Pasha: School of Information Technology, Monash University, Subang Jaya 47500, Malaysia
David T. Smith: Computer and Information Sciences, Virginia Military Institute, Lexington, VA 24450, USA
Imran Ghani: Computer and Information Sciences, Virginia Military Institute, Lexington, VA 24450, USA
Muhammad Asif: Architectural Engineering Department, School of Engineering and Built Environment, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Seung Ryul Jeong: Graduate School of Business IT, Kookmin University, Seoul 05029, Republic of Korea
Moveh Samuel: Department of Aeronautical Engineering, Istanbul Gelisim University, 34310 Istanbul, Turkey

Energies, 2022, vol. 15, issue 23, 1-22

Abstract: Recent advancements in the Internet of Things and Machine Learning techniques have allowed the deployment of sensors on a large scale to monitor the environment and model and predict individual thermal comfort. The existing techniques have a greater focus on occupancy detection, estimations, and localization to balance energy usage and thermal comfort satisfaction. Different sensors, actuators, and analytic data methods are often non-invasively utilized to analyze data from occupant surroundings, identify occupant existence, estimate their numbers, and trigger the necessary action to complete a task. The efficiency of the non-invasive strategies documented in the literature, on the other hand, is rather poor due to the low quality of the datasets utilized in model training and the selection of machine learning technology. This study combines data from camera and environmental sensing using interactive learning and a rule-based classifier to improve the collection and quality of the datasets and data pre-processing. The study compiles a new comprehensive public set of training datasets for building occupancy profile prediction with over 40,000 records. To the best of our knowledge, it is the largest dataset to date, with the most realistic and challenging setting in building occupancy prediction. Furthermore, to the best of our knowledge, this is the first study that attained a robust occupancy count by considering a multimodal input to a single output regression model through the mining and mapping of feature importance, which has advantages over statistical approaches. The proposed solution is tested in a living room with a prototype system integrated with various sensors to obtain occupant-surrounding environmental datasets. The model’s prediction results indicate that the proposed solution can obtain data, and process and predict the occupants’ presence and their number with high accuracy values of 99.7% and 99.35%, respectively, using random forest.

Keywords: smart buildings; energy; indoor; occupancy; machine learning; carbon dioxide (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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