Authors:
Luis E. Zárate
1
;
Elizabeth Marques Duarte Pereira
2
;
Daniel Alencar Soares
1
;
João Paulo D. Silva
1
;
Renato Vimieiro
1
and
Antonia Sonia Cardoso Diniz
3
Affiliations:
1
Applied Computational Intelligence Laboratory (LICAP), Pontifical Catholic University of Minas Gerais (PUC), Brazil
;
2
Energy Researches Group (GREEN), Pontifical Catholic University of Minas Gerais (PUC), Brazil
;
3
Energy Company of Minas Gerais (CEMIG), Pontifical Catholic University of Minas Gerais (PUC), Brazil
Keyword(s):
Artificial Intelligence, Artificial Neural Networks, Solar Energy, Clustering, Thermosiphon.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Network Software and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Due to the necessity of new ways of energy producing, solar collector systems have been widely used around the world. There are mathematical models that calculate the efficiency of those systems; however these models involve several parameters that may lead to nonlinear equations of the process. Artificial Neural Networks have been proposed in this work as an alternative of those models. However, a better modeling of the process by means of ANN depends on a representative training set; thus, in order to better define the training set, the clustering technique called k-means has been used in this work.