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Developing Data-Driven Occupancy Detection Models Based on Individual Plug Load Profiles in Office Spaces
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摘要
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To analyze the human-building interaction, it is crucial to accurately detect the presence of occupants inside buildings. Current occupancy detection systems usually require expensive and complex sensors, while individual occupancy detection algorithms might not provide enough accuracy. This study aims to develop a cost-effective occupancy detection model using machine learning algorithms based on individual plug load data. The models are trained to detect real-time occupancy, validated by a battery- operated seat pressure sensor in an office space test bed. The test bed was a student office station at Louisiana State University, equipped with energy-monitoring wallplugs and seat pressure sensors. Two weeks of data were collected and used for training support vector machine and artificial neural network models. The developed models are also validated using the ASHRAE occupant behavior plug-in device load database in a public building located in the US. The results showed an accuracy of 98.72% in detecting occupant presence in real-time using plug load profiles.
Keyword:
ELECTRICITY CONSUMPTION
BUILDINGS
