Building Occupancy Detection from Carbon-dioxide and Motion Sensors
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The occupancy detection problem is formulated as a classification problem wherein the classifier learns from offline carbon-dioxide data and the actual occupancy measurements of the room. While the classifier can provide realtime occupancy detection, the delays in carbon-dioxide sensors influence their accuracy. To overcome the delays, observations from PIR sensors are combined with the results of the single-layer feedforward neural network (SLFN) based classifier. The classifier works in four steps: (i) data-preprocessing, (ii) feature-selection, (iii) learning, and (iv) validation. The data is preprocessed by smoothing and several features are selected as input to the SLFN. Then, the classifier is validated with realtime experiments.