Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/80276
Title: Estimation of classrooms occupancy using a multi-layer perceptron
Authors: Eugénio Rodrigues 
Luísa Dias Pereira 
Adélio Rodrigues Gaspar 
Álvaro Gomes 
Manuel Carlos Gameiro da Silva 
Keywords: Computer Science - Neural and Evolutionary Computing; Computer Science - Neural and Evolutionary Computing; Computer Science - Learning
Issue Date: 7-Feb-2017
Keywords: Computer Science - Neural and Evolutionary Computing; Computer Science - Neural and Evolutionary Computing; Computer Science - Learning
Issue Date: 7-Feb-2017
Project: Ren4EEnIEQ (PTDC/EMS-ENE/3238/2014, POCI-01-0145-FEDER-016760, LISBOA-01-0145-FEDER-016760) 
SFRH/BPD/99668/2014 
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.
URI: http://arxiv.org/abs/1702.02125v1
http://hdl.handle.net/10316/80276
Rights: openAccess
Appears in Collections:FCTUC Eng.Mecânica - Artigos e Resumos em Livros de Actas

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