Browsing by Author "Martins, P. M."
Now showing 1 - 3 of 3
Results Per Page
Sort Options
- Model comparison for temperature estimation inside buildingsPublication . Crispim, E. M.; Martins, P. M.; Ruano, AntonioThis 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.
- Neural networks applied to temperature estimation in school buildingsPublication . Crispim, E. M.; Martins, P. M.; Ruano, AntonioThis paper present an Artificial Neural Network (NN) applied to the modelling of inside air temperature in a building school. This modelling is a function of outside air temperature and solar radiation, inside air humidity and state of windows and doors. This NN is a one step-ahead predictive model, and is intended to be the basis model for longer prediction horizons. The NN model employed was the Radial Basis Functions Neural Network (RBFNN, trained using the Levenberg-Maquardt algorithm. The structure selection of the best fitted model RBFNN was accomplished by multiobjective genetic algorithms (MOGA).
- Remote data acquisition system of environmental dataPublication . Crispim, E. M.; Martins, P. M.; Ruano, Antonio; Fonseca, Carlos M.This paper presents the implementation of an environmental data acquisition system in a school building. The chosen building is a secondary school, located in Estoi, Portugal. The purpose of this data acquisition system is to collect environmental information from inside and outside. In the implementation of this system were employed the network infrastructure of the building and radio frequency communications. Using Internet facilities the data stored in a local computer is transferred to the main server located in the University of Algarve. Developed web tools in the main server allow access to data and administration features.