Robust, Fine-Grained Occupancy Estimation via Combined Camera &amp; WiFi Indoor Localization

Ravi, Anuradha; Misra, Archan

doi:10.1109/mass50613.2020.00074

Cited by 1 publication

(1 citation statement)

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“…Ravi et al, proposed an algorithm for estimating the occupancy by matching occupancy counts measured using a camera and WiFi connection counts by area. In the case of WiFi-alone estimation, there was an average error of 63.4%, and in the case of camera-based estimation, there was an average error of 25.3%; however, the error was reduced to an average of 16.2% by applying the estimation algorithm [ 13 ]. Wang et al, collected environmental sensor data and WiFi connection data and then confirmed the correlation and error between the data collected using the adaptive snare model and actual occupancy data collected by the camera.…”

Section: Introductionmentioning

confidence: 99%

Occupancy-Based Energy Consumption Estimation Improvement through Deep Learning

Kim

Park

Son

2023

Sensors

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The energy consumed in buildings constitutes more than half of the total electricity consumption and is highly correlated with the number of occupants; therefore, it is necessary to use occupancy information in energy consumption analysis. However, the number of occupants may not be accurate owing to measurement errors caused by various factors, such as the locations of sensors or cameras and the communication environment. In this study, occupancy was measured using an object recognition camera, the number of people was additionally collected by manual aggregation, measurement error in occupancy count was analyzed, and the true count was estimated using a deep learning model. The energy consumption based on occupancy was predicted using the measured and estimated values. To this end, deep learning was used to predict energy consumption after analyzing the correlation between occupancy and energy consumption. Results showed that the performance of occupancy estimation was 1.9 based on RMSE, which is a 71.1% improvement compared to the original occupancy sensing. The RMSE of predicted energy consumption based on the estimated occupancy was 56.0, which is a 5.2% improvement compared to the original energy estimation.

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Section: Introductionmentioning

confidence: 99%