Comparing TSK-1 FRBS against SVR for electrical power prediction in buildings
- https://doi.org/10.2991/ifsa-eusflat-15.2015.124How to use a DOI?
- Support vector regression, Takagi Sugeno Kang fuzzy rule based system, power prediction, buildings electrical power.
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/ifsa-eusflat-15.2015.124 ID - Cózar2015/06 ER -