OCCUPANCY ESTIMATION METHOD BASED ON CARBON DIOXIDE CONCENTRATION USING DYNAMIC NEURAL NETWORK

摘要

The present invention relates to an occupancy estimation method based on carbon dioxide concentration using a dynamic neural network. An occupancy estimation method based on carbon dioxide concentration using a dynamic neural network includes a measuring step of measuring carbon dioxide concentration included in a room atmosphere; a storing step of storing the carbon dioxide concentration measured by the measuring step in real time; a configuring step of configuring at least one dynamic neural network to estimate the number of remaining occupants in a room based on the carbon dioxide concentration stored by the storing step; a neural network training step of comparing the number of remaining occupants, which is estimated by the dynamic neural network configured by the configuring step, with a reference value previously input for the number of remaining occupants, and comparing an error between the number of remaining occupants and the reference value with a preset threshold value in order to control a weight value and a deviation value of a transfer function for configuring the dynamic neural network to allow the error to be decreased; and an estimating step of estimating the number of remaining occupants through the dynamic neural network controlled by the neural network training step. Thus, the present invention proposes a technique of efficiently recognizing the number of occupants in a building. According to the present invention, as compared with those of a occupancy confirming method using a video camera or a motion recognition sensor according to the related art, the installation and maintenance costs are inexpensive and there is no blind spot so that the reliability is improved.