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IoT-based occupancy estimation models for indoor non-residential environmentsPublished in Institute of Electrical and Electronics Engineers Inc.
2019
An IoT end to end system has been developed in this work to collect relative humidity (RH), CO2 concentration and occupant count of a University classroom. The RH and CO2 data has been used to compute estimates of student occupancy using regression based estimation models. Multiple linear and quantile regression models have been explored for occupancy estimation by using RH, CO2, and both RH as well as CO2 concentration respectively. The estimation performance of these models has been compared by using mean absolute percentage error. The quantile regression based models have been found to be the most accurate with a mean absolute percentage error of 2.47\%. © 2019 IEEE.
Topics: Mean absolute percentage error (60)%60% related to the paper and Quantile regression (52)%52% related to the paper
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Published in Institute of Electrical and Electronics Engineers Inc.
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