Data-Driven Approach for Residential Occupancy Modeling Using PIR Sensors: A Moroccan Case Study

Presence detection and its use in home automation systems can significantly save energy by automatically controlling lighting and HVAC systems. However, to achieve these advantages, we present an approach that combines data mining techniques with Passive Infrared (PIR) sensor data to track the presence and movement patterns in a living laboratory to develop a more accurate occupancy model. The proposed method involves data processing, feature extraction, and the use of a Markov chain model to create an occupancy model that detects and aligns zone occupancy with a sidewalk detection approach that can improve occupancy and energy efficiency. enabling more targeted system management. Overall, the results of this study show that PIR sensors have great potential for pedestrian detection and occupancy modeling in residential buildings. Using the proposed method, more efficient and effective energy management systems could be created, and the comfort and safety of living in residential buildings could be improved.

In International Conference on Cloud Technologies and Applications