A Hybrid Regression System Based on Local Models for Solar Energy Prediction

Article type: Research Article

Authors: Quintián, Héctor^; | Calvo-Rolle, Jose Luis | Corchado, Emilio

Affiliations: University of Salamanca, Salamanca, Spain, e-mail: hector.quintian@usal.es, jlcalvo@udc.es, escorchado@usal.es

Note: [] Corresponding author.

Abstract: 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.

Keywords: hybrid system, clustering, local models, SOM, MLP, SVM, prediction solar energy

Journal: Informatica, vol. 25, no. 2, pp. 265-282, 2014