Using Stream Data Processing for Real-Time Occupancy Detection in Smart Buildings

Abstract

Controlling active and passive systems in buildings with the aim of optimizing energy efficiency and maintaining occupants’ comfort is the major task of building management systems. However, most of these systems use a predefined configuration, which usually do not match the occupants’ preferences. Therefore, occupancy detection is imperative for energy use management mainly in residential and industrial buildings. Most works related to data-driven-based occupancy detection have used batch learning techniques, which need to store first and then train the data. It is not appropriate for a non-stationary environment. Therefore, this work sheds more light on the use of non-stationary machine learning techniques. To this end, three machine learning algorithms for stream data processing are presented, tested, and evaluated in term of accuracy and resources performance (i.e., RAM, CPU), with the aim of predicting the number of occupants in smart buildings. A platform architecture that integrates IoT technologies with stream machine learning is implemented and deployed. The experimental results show the effectiveness of this approach and illustrate that the number of occupants can be predicted with an accuracy of more than 83% and without resource wasting (i.e., time of CPU use varied between 0.04s and 3.85 ⋅ 10−11 GB of RAM could be exploited per hour).

Occupancy Measurement in Under-Actuated Zones: YOLO-based Deep Learning Approach

“…For instance, analyzing an image with the bounding box regressor in Faster R-CNN can take around 50 seconds. Moreover, Faster R-CNN is a resource-intensive approach, necessitating substantial storage for feature maps across all regions [23]. This requirement leads to a considerable storage demand, often in the hundreds of gigabytes, due to the need to cache extracted features from the pre-trained CNN on disk for subsequent SVM training [22].…”

“…To address these challenges, camera systems require advanced features and optimized frame rates to accurately count and track occupants across varied scenarios, from low-activity environments to areas with high occupancy and dynamic movement patterns [22]. The unpredictability and diversity of occupant dynamics in under-actuated zones further necessitate the deployment of sophisticated algorithms for data processing and analysis [23]. These algorithms must be capable of interpreting complex and varied data to ensure effective tracking and counting of occupants.…”

Internet-of-Things Based Hardware-in-the-Loop Framework for Model-Predictive-Control of Smart Building Ventilation

“…Further, Zhang et al [ 57 ] presented a literature review about the integration of machine learning for predicting occupancy patterns to improve indoor air quality, while optimizing energy use. In addition, online machine learning techniques (e.g., vertical Hoeffding tree and self-adjusting memory for KNN) can be included for predicting occupants’ number and presence using environmental data, such as CO 2 temperature and humidity [ 58 , 59 ]. IoT and HIL concepts could provide an integrated solution to cover the important aspects of BEMS by enabling the collection, monitoring, and processing of stream data together with machine-learning techniques.…”

A Non-Intrusive Method for Lonely Death Prevention Using Occupancy Detection and an Anomaly Detection Model

“…Aliero et al [10] compiled a public set of training datasets for building occupancy profile prediction and compared occupancy prediction accuracies of five ML algorithms. The authors of [11] introduced a novel platform architecture integrating an IoT platform to collect sensors' streaming data and machine learning algorithms implemented in the server site for application to streaming and non-stationary data. While numerous studies have focused on occupancy detection to enhance energy management in buildings [12][13][14][15][16][17], none have specifically gathered occupancy data and utilized ML models to infer participants' behaviors to predict risk situations for older adults living alone.…”