Modeling indoor air carbon dioxide concentration using artificial neural network

Abstract

Many studies have been conducted on estimating the number of occupants in a building to set the right ventilation rate in order to maintain standard indoor air quality. However, few have focused on predicting carbon dioxide itself based on the room’s available parameters, such as temperature and humidity. This study was aimed at predicting indoor air carbon dioxide concentration in a room using a multilayer perceptron neural network with relative humidity and temperature as inputs. The neural network is a popular data-driven method to provide geometry-independent prediction algorithms. In this study, the neural network was trained in three different ways with the complete, partial, and zero real carbon dioxide concentrations available in the learning process. The sensitivity and specificity analyses were conducted on the output. The most accurate model, based on the calculated mean-square-error method, was five-steps-ahead prediction model with less than 17 PPM difference on average to actual CO2 concentration in the room. Results were also promising for the open-loop model. Carbon dioxide predictions can be used in maintaining indoor air quality by improving ventilation control in buildings.