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, Moveh Samuel

Research output: Contribution to journalArticleResearchpeer-review

14 Citations (Scopus)

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.

Original languageEnglish
Article number9231
Number of pages22
JournalEnergies
Volume15
Issue number23
DOIs
Publication statusPublished - Dec 2022

Keywords

  • carbon dioxide
  • energy
  • indoor
  • machine learning
  • occupancy
  • smart buildings

Cite this