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Title: Estimation of classrooms occupancy using a multi-layer perceptron
Authors: Rodrigues, Eugénio 
Pereira, Luísa Dias 
Gaspar, Adélio Rodrigues 
Álvaro Gomes 
Silva, Manuel Carlos Gameiro da 
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) 
Serial title, monograph or event: EfS 2017, Energy for Sustainability International Conference 2017: Designing Cities & Communities for the Future. Funchal, 8-10 February
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.
Rights: openAccess
Appears in Collections:FCTUC Eng.Mecânica - Artigos e Resumos em Livros de Actas

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