A neural network approach to the identification of room occupationalness according to air parameters

Abstract

The paper introduces an approach to determining the number of people in a room based on data from observations of air parameters using a multilayer neural network. Monitoring of air parameters, the ability to predict their values and manage ventilation systems are important to ensure a healthy and comfortable indoor environment. The purpose of the research is to develop mathematical models and methods of forecasting air parameters based on the results of real-time monitoring. Different approaches to pre-dicting air parameters and the number of people in rooms using mathematical models in the form of equations and artificial neural networks with different architectures and types of training functions are considered. The paper proposes an approach to forecasting with the help of a multilayer neural network, which allows taking into account various factors, the nature and quantitative values of which cannot be taken into account and cannot be added to the model parameters. The CO2 data together with the indoor occupancy data from the meters are used to train the neural network. In the future, a neural network trained on such data can be used to predict the number of people in a room based only on data from air quality analyzers. The issue of choosing the architecture of a multilayer neural network and its pa-rameters for solving the forecasting problem has been investigated. Neural network training is carried out by the method of error back propagation. To evaluate the forecast-ing quality of the model, the metrics of mean square error of forecast and mean absolute error of forecast are used. The Kruskal-Wallis criterion is used to take into account the results of forecasting on several samples. Based on the results of the computational ex-periment, the optimal network architecture is determined. The model successfully repro-duces the nature of the distribution of the predicted parameter, as it captures the com-plex nonlinear dependencies between the various factors of the model. Recommendations are given to improve the performance of the model.