Informatica

The aim of this study is to predict the energy generated by a solar thermal system. To achieve this, a hybrid intelligent system was developed based on local regression models with low complexity and high accuracy. Input data is divided into clusters by using a Self Organization Maps; a local model will then be created for each cluster. Different regression techniques were tested and the best one was chosen. The novel hybrid regression system based on local models is empirically verified with a real dataset obtained by the solar thermal system of a bioclimatic house.

In this article we present the general architecture of a hybrid neuro-symbolic system for the selection and stepwise elimination of predictor variables and non-relevant individuals for the construction of a model. Our purpose is to design tools for extracting the relevant variables and the relevant individuals for an automatic training from data. The objective is to reduce the complexity of storage, therefore the complexity of calculation, and to gradually improve the performance of ordering, that is to say to arrive at a good quality training.