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

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.