Loading...
9 results
Search Results
Now showing 1 - 9 of 9
- Predicting solar radiation with RBF neural networksPublication . Ferreira, P. M.; Ruano, AntonioIn this paper radial basis function neural networks are applied to the prediction of global solar radiation. The networks are employed as one-step-ahead predictors of the solar radiation time series and iterated over time to obtain longer term predictions. Several models are compared varying the input dimension, the network size and the time series sampling rate. An empiric rule is proposed for network input selection. All networks are trained using one data set and evaluated for prediction performance on unseen data. Predictor performance is assessed taking root mean square measures of the error over the prediction horizon. The aim of this work is to select a model to be used in a climate simulator for an hydroponic greenhouse.
- Comparison of on-line learning algorithms for RBF models in greenhouse environmental control problemsPublication . Ferreira, P. M.; Faria, E. A.; Ruano, AntonioThe problem with the adequacy of radial basis function neural networks to model the inside air temperature as a function of the outside air temperature and solar radiation, and the inside relative humidity in an hydroponic greenhouse is addressed.
- Application of radial basis function neural networks to a greenhouse inside air temperature modelPublication . Ferreira, P. M.; Faria, E. A.; Ruano, AntonioThe problem with the adequacy of radial basis function neural networks to model the inside air temperature as a function of the outside air temperature and solar radiation, and the inside relative humidity in an hydroponic greenhouse is addressed.
- Greenhouse air temperature modelling with radial basis function neural networksPublication . Ferreira, P. M.; Ruano, AntonioResults on the application of radial basis function neural networks to model the inside air temperature of a hydroponic greenhouse as a function of the outside air temperature and solar radiation, and the inside relative humidity, are presented. As the model is intended to be incorporated in an predictive control strategy both off-line and on-line methods are important to accomplish this task. In this paper hybrid off-line training methods and on-line learning algorithms are analysed. Results from a previously presented off-line method and its application to on-line learning are also presented. It exploits the linear-nonlinear structure found in radial basis function neural networks.
- Choice of RBF model structure for predicting greenhouse inside air temperaturePublication . Ferreira, P. M.; Ruano, AntonioThe application of the radial basis function neural network to greenhouse inside air temperature modelling has been previously investigated by the authors. In those studies, the inside air temperature is modelled as a function of the inside relative humidity and of the outside temperature and solar radiation. Several training and learning methods were compared and the application of the Levenberg-Marquardt optimisation method was found to be the best way to determine the neural network parameters. A second-order model structure previously selected in the context of dynamic temperature models identification, was used. The model is intended to be incorporated in a real-time predictive greenhouse environmental control strategy. It is now relevant to question if the model structure used so far, selected in a different modelling framework, is the most correct in some sense. In this paper the usefulness of correlation-based model validity tests is addressed in order to answer the question mentioned above.
- Evolving RBF predictive models to forecast the Portuguese electricity consumptionPublication . Ferreira, P. M.; Ruano, Antonio; Pestana, Rui; Kóczy, László T.The Portuguese power grid company wants to improve the accuracy of the electricity load demand (ELD) forecast within an horizon of 24 to 48 hours, in order to identify the need of reserves to be allocated in the Iberian Market. In this work we present some preliminary results about the identi cation of radial basis function (RBF) neural network (NN) ELD predictive models and about the performance of a model selection algorithm. The methodology follows the principles already employed by the authors in di erent applications: the NN models are trained by the Levenberg-Marquardt algorithm using a modi ed training criterion, and the model structure (number of neurons and input terms) is evolved using a Multi-Objective Genetic Algorithm (MOGA). The set of goals and objectives used in the MOGA model optimisation reflect different requirements in the design: obtaining good generalisation ability, good balance between one-step-ahead prediction accuracy and model complexity, and good multi-step prediction accuracy. A number of experiments were carried out, whose results are presented, producing already a number of models whose predictive performance is satisfactory.
- Training neural networks and neuro-fuzzy systems: a unified viewPublication . Ruano, Antonio; Ferreira, P. M.; Cabrita, Cristiano Lourenço; Matos, S.Neural and neuro-fuzzy models are powerful nonlinear modelling tools. Different structures, with different properties, are widely used to capture static or dynamical nonlinear mappings. Static (non-recurrent) models share a common structure: a nonlinear stage, followed by a linear mapping. In this paper, the separability of linear and nonlinear parameters is exploited for completely supervised training algorithms. Examples of this unified view are presented, involving multilayer perceptrons, radial basis functions, wavelet networks, B-splines, Mamdani and TSK fuzzy systems.
- Predicting the greenhouse inside air temperature with RBF neural networksPublication . Ferreira, P. M.; Ruano, AntonioThe application of the Radial Basis Function (RBF) Neural Network (NN) to greenhouse inside air temperature modelling has been previously investigated (Ferreira et al., 2000a). In those studies, the inside air temperature is modelled as a function of the inside relative humidity and of the outside temperature and solar radiation. A second-order model structure previously selected (Cunha et al., 1996) in the context of dynamic temperature models identification, is used.
- Cloud and clear sky pixel classification in ground-based all-sky hemispherical digital imagesPublication . Ferreira, P. M.; Martins, I.; Ruano, AntonioCloudiness is the non-predictable factor most a ecting the solar radiation reaching a particular location on the Earth surface. Therefore it has great impact on the performance of predictive solar radiation models for that location. This work represents one step towards the improvement of such models by using ground-to-sky hemispherical colour digital images as a means to estimate the fraction of visible sky corresponding to clouds and to clear sky. The general approach, common to many image processing applications, consists in finding one threshold on a given pixel intensity scale that segments the image pixels into clear sky and cloud. In order to allow the evaluation and comparison of image thresholding methods, the pixels of 410 images were manually classified as clear sky or cloud, establishing a reference database. Two well known image thresholding algorithms are tested and a neural network approach is presented. For the latter, a number of statistical measures is extracted from the images constituting a feature space of potential inputs for the neural network. The actual inputs and number of neurons to be employed are selected by means of a multi-objective genetic algorithm.