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

Abstract

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 CO2 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.

Keywords

• Activities recognition
• building performance
• data mining
• human behavior
• machine learning
• office buildings