  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.
