Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/2296
Título: Model comparison for temperature estimation inside buildings
Autor: Crispim, E. M.
Martins, P. M.
Ruano, A. E.
Palavras-chave: Estimation
Feedforward neural networks
Genetic algorithms
Nonlinear systems
Data: 2005
Editora: IEEE
Citação: Crispim, E. M.; Martins, P. M.; Ruano, A. E. Model comparison for temperature estimation inside buildings, Trabalho apresentado em IEEE International Workshop on Soft Computing Applications, In Proceedings of the IEEE International Workshop on Soft Computing Applications, Szeged, 2005.
Resumo: This paper presents a comparison between a physical model and an artificial neural network model (NN) for temperature estimation inside a building room. Despite the obvious advantages of the physical model for structure optimisation purposes, this paper will test the performance of neural models for inside temperature estimation. The great advantage of the NN model is a big reduction of human effort time, because it is not needed to develop the structural geometry and structural thermal capacities and to simulate, which consumes a great human effort and great computation time. The NN model deals with this problem as a “black box” problem. We describe the use of the Radial Basis Function (RBF), the training method and a multi-objective genetic algorithm for optimisation/selection of the RBF neural network inputs and number of neurons.
Peer review: yes
URI: http://hdl.handle.net/10400.1/2296
Aparece nas colecções:FCT2-Artigos (em revistas ou actas indexadas)

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