Comparing TSK-1 FRBS against SVR for electrical power prediction in buildings

DOI

10.2991/ifsa-eusflat-15.2015.124How to use a DOI?

Keywords

Support vector regression, Takagi Sugeno Kang fuzzy rule based system, power prediction, buildings electrical power.

Abstract

The study of energy efficiency in buildings is an active field of research. Modelling and predicting energy related magnitudes leads to analyse electric power consumption and can achieve economical benefits. In this study, machine learning techniques are applied to predict active power in buildings. The real data acquired corresponds to time, environmental and electrical data of 30 buildings belonging to the University of León (Spain). Firstly, we segmented buildings in terms of their energy consumption using principal component analysis. Afterwards, after test different univariate and multivariate techniques, we applied SVR and a learning FRBS method to compare their performance. Models were studied for different variable selections. Our analysis shows that the FRBS has the lowest error needing a similar learning time than SVR.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Javier Cózar
AU  - Gonzalo Vergara
AU  - José A. Gámez
AU  - Emilio Soria-Olivas
PY  - 2015/06
DA  - 2015/06
TI  - Comparing TSK-1 FRBS against SVR for electrical power prediction in buildings
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 880
EP  - 887
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.124
DO  - 10.2991/ifsa-eusflat-15.2015.124
ID  - Cózar2015/06
ER  -