Browsing by Author "Crispim, E. M."
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- Controlo inteligente de sistemas AVAC - tendências actuaisPublication . Ruano, Antonio; Crispim, E. M.In EU countries, the buildings power consumption represents about 40% of the total power consumption. In some countries, half of this energy is spent in climatization. Estimation shows that using an efficient climatization management system can save about 20% of such energy.
- Development of a temperature control model used in HVAC systems in school spaces in Mediterranean climatePublication . Conceição, Eusébio; Lúcio, Maria Manuela Jacinto do Rosário; Ruano, Antonio; Crispim, E. M.In this paper a temperature control model used in heating, Ventilating and air-conditioning (HVAC) systems in school spaces, in Mediterranean climate, is developed. This empirical model considers the indoor preferred environmental temperature, the outdoor environmental temperature and the adaptation to the seasons of the year and to the spaces. In the development of the empirical model, in a school building located in the Algarve region, in the South of Portugal, occupied spaces by the non-teacher staff (administrative and auxiliary), teachers and students were used. In these spaces, equipped with heating, ventilating and air-conditioning systems, the occupants can change, during one year of school activities, the indoor environmental conditions in order to obtain acceptable comfort conditions. The indoor air temperature and relative humidity inside four spaces, namely an office room, a secretary room, a teachers room and a library room, the outdoor air temperature and other parameters related to the spaces and heating, ventilating and air-conditioning systems were measured every minute during one year. This empirical model, based in a group of equations for different months and for several spaces, gives information that can be used in the control of a heating, ventilating and air-conditioning systems, in school spaces, in Mediterranean climate, in order to promote indoor acceptable conditions with energy savings. In this model the adaptation to the outdoor environment, for different seasons, the clothing level and the fact that occupants enter and leave frequently from spaces equipped with heating, ventilating and air-conditioning systems to others not equipped are analyzed.
- 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.
- MOGA design of neural network predictors of inside temperature in public buildingsPublication . Ruano, Antonio; Crispim, E. M.; Frazão, P. M.The use of artificial neural networks in various applications related with energy management in buildings has been increasing significantly over the recent years. In this chapter, the design of inside air temperature predictive neural network models, to be used for predictive thermal comfort control, is discussed. The design is based on the joint use of multi-objective genetic (MOGA) algorithms, for selecting the network structure and the network inputs, and a derivative algorithm, for parameter estimation. Climate and environmental data from a secondary school located in the south of Portugal, collected by a remote data acquisition system, are used to generate the models. By using a sliding window adaptive methodology, the good results obtained off-line are extended throughout the whole year.
- 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).
- Prediction of building's temperature using neural networks modelsPublication . Ruano, Antonio; Crispim, E. M.; Conceição, Eusébio; Lúcio, Maria Manuela Jacinto do RosárioThe use of artificial neural networks in various applications related with energy management in buildings has been increasing significantly over the recent years. In this paper the design of inside air temperature predictive neural network models, to be used for predictive control of airconditioned systems, is discussed. The use of multi-objective genetic algorithms for designing off-line radial basis function neural network models is detailed. The performance of these data-driven models is compared, favourably, with a multi-node physically based model. Climate and environmental data from a secondary school located in the south of Portugal, collected by a remote data acquisition system, are used to generate the models. By using a sliding window adaptive methodology, the good results obtained off-line are extended throughout the whole year. The use of long-range predictive models for airconditioning systems control is demonstrated, in simulations, achieving a good temperature regulation with important energy savings.
- Prediction of the solar radiation evolution using computational intelligence techniques and cloudiness indicesPublication . Ruano, Antonio; Crispim, E. M.; Ferreira, P. M.In this paper, Artificial Neural Networks are applied for multi-step long term solar radiation prediction. The input-output structure of the neural network models is selected using evolutionary computation methods. The networks are trained as onestep- ahead predictors and iterated over time to obtain multi-step longer term predictions. Auto-regressive and auto-regressive with exogenous inputs models are compared, considering cloudiness indices as inputs in the latter case. These indices are obtained through pixel classification of ground-to-sky images, captured by a CCD camera.
- Prediction of the solar radiation using RBF neural networks and ground-to-sky imagesPublication . Crispim, E. M.; Ferreira, P. M.; Ruano, AntonioIn this study, Artificial Neural Networks are applied to multistep long term solar radiation prediction. The networks are trained as one-step-ahead predictors and iterated over time to obtain multi-step longer term predictions. Auto-regressive and Auto-regressive with exogenous inputs solar radiationmodels are compared, considering cloudiness indices as inputs in the latter case. These indices are obtained through pixel classification of ground-to-sky images. The input-output structure of the neural network models is selected using evolutionary computation methods.
- 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.
- Solar radiation prediction using RBF neural networks and cloudiness indicesPublication . Crispim, E. M.; Ferreira, P. M.; Ruano, AntonioIn this paper, Artificial Neural Networks are applied to multi-step long term solar radiation prediction. The networks are trained as one-step-ahead predictors and iterated over time to obtain multi-step longer term predictions. Auto-regressive and Auto-regressive with exogenous inputs solar radiation models are compared, considering cloudiness indices as inputs in the latter case. These indices are obtained through pixel classification of ground-to-sky images. The input-output structure of the neural network models is selected using evolutionary computation methods.