Estimating Occupancy Using Interactive Learning With a Sensor Environment: Real-Time Experiments

Interactive learning plays a key role in extending the occupant behavior implementation toward smart buildings. Efficient feedbacks can be obtained from the end user by involving occupants and increasing their awareness about energy systems. Working in highly energy-efficient buildings can be a grea...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.53932-53944
Main Authors: Amayri, Manar, Ploix, Stephane, Bouguila, Nizar, Wurtz, Frederic
Format: Article
Language:English
Subjects:
Activities recognition
Algorithms
Analytical models
building performance
Classifiers
Cognitive science
Computer science
data mining
Data models
Decision trees
Disorientation
Estimation
Graphical user interface
Hidden Markov models
human behavior
Interactive learning
Machine Learning
Motion perception
Occupancy
office buildings
Power consumption
Real time
Smart buildings
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Summary:Interactive learning plays a key role in extending the occupant behavior implementation toward smart buildings. Efficient feedbacks can be obtained from the end user by involving occupants and increasing their awareness about energy systems. Working in highly energy-efficient buildings can be a great opportunity, but users need to feel empowered. This means making them aware of the building features and allowing them to manage some of the appliances. In this way, disorientation or annoyance is avoided, and people feel more in control. This paper proposes a solution to interact with occupants to estimate the number of occupants. A novel way of supervised learning is analyzed to estimate the occupancy in a room where actual occupancy is interactively requested to occupants when it is the most relevant to limit the number of interactions. Occupancy estimation algorithm relies on machine learning; it uses information gathered from occupants with the measurements collected from common sensors, for instance, motion detection, power consumption, and CO 2 concentration. Two different classifiers are investigated for occupancy estimation with interactions: a decision tree C4.5 and a parameterized rule-based classifier. In this paper, the question of when interacting with occupants is investigated. This approach avoids the usage of a camera to determine the actual occupancy. A complete real-time interaction environment has been developed and is used to estimate the occupancy in an office case study. The graphical user interface has been designed to carry out a real-time experiment.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2911921