0000000001091543

AUTHOR

Cristina Romero-gonzález

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Comparing ELM Against MLP for Electrical Power Prediction in Buildings

2015

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 Leon (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…

Computer sciencebusiness.industryEnergy consumptionAC powerMachine learningcomputer.software_genreField (computer science)Multilayer perceptronPrincipal component analysisArtificial intelligenceElectric powerbusinesscomputerEnergy (signal processing)Efficient energy use
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