Estimation of classrooms occupancy using a multi-layer perceptron

TL;DR

This study develops a multi-layer perceptron model to estimate classroom occupancy using environmental sensors, accurately reconstructing occupancy during unreported periods with high precision.

Contribution

The paper introduces a novel neural network approach that accurately estimates classroom occupancy from environmental data, filling gaps in occupancy reporting.

Findings

Mean square error of 1.99 in occupancy estimation

Coefficient of determination of 0.96 indicating high model accuracy

Mean absolute error of 1 occupant

Abstract

This paper presents a multi-layer perceptron model for the estimation of classrooms number of occupants from sensed indoor environmental data-relative humidity, air temperature, and carbon dioxide concentration. The modelling datasets were collected from two classrooms in the Secondary School of Pombal, Portugal. The number of occupants and occupation periods were obtained from class attendance reports. However, post-class occupancy was unknown and the developed model is used to reconstruct the classrooms occupancy by filling the unreported periods. Different model structure and environment variables combination were tested. The model with best accuracy had as input vector 10 variables of five averaged time intervals of relative humidity and carbon dioxide concentration. The model presented a mean square error of 1.99, coefficient of determination of 0.96 with a significance of p-value < 0.001, and a mean absolute error of 1 occupant. These results show promising estimation capabilities in uncertain indoor environment conditions.