Utilize este identificador para referenciar este registo: https://hdl.handle.net/10316/80276
Título: Estimation of classrooms occupancy using a multi-layer perceptron
Autor: Rodrigues, Eugénio 
Pereira, Luísa Dias 
Gaspar, Adélio Rodrigues 
Álvaro Gomes 
Silva, Manuel Carlos Gameiro da 
Palavras-chave: Computer Science - Neural and Evolutionary Computing; Computer Science - Neural and Evolutionary Computing; Computer Science - Learning
Data: 7-Fev-2017
Projeto: Ren4EEnIEQ (PTDC/EMS-ENE/3238/2014, POCI-01-0145-FEDER-016760, LISBOA-01-0145-FEDER-016760) 
SFRH/BPD/99668/2014 
Título da revista, periódico, livro ou evento: EfS 2017, Energy for Sustainability International Conference 2017: Designing Cities & Communities for the Future. Funchal, 8-10 February
Resumo: 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: https://arxiv.org/abs/1702.02125v1
https://hdl.handle.net/10316/80276
Direitos: openAccess
Aparece nas coleções:FCTUC Eng.Mecânica - Artigos e Resumos em Livros de Actas

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