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, two 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 we applied ELM and MLP methods to compare their performance. Models were studied for different variable selections. Our analysis shows that the MLP obtains the lowest error but also higher learning time than ELM.
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Vergara, G., Cózar, J., Romero-González, C., Gámez, J.A., Soria-Olivas, E. (2015). Comparing ELM Against MLP for Electrical Power Prediction in Buildings. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Bioinspired Computation in Artificial Systems. IWINAC 2015. Lecture Notes in Computer Science(), vol 9108. Springer, Cham. https://doi.org/10.1007/978-3-319-18833-1_43
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DOI: https://doi.org/10.1007/978-3-319-18833-1_43
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