Abstract
Geothermal exchangers are among the most interesting solutions to equip a modern house with a renewable energy heating installation. The present study shows the computational modelling of an instance of an installation of such type, aiming to predict the behaviour of the system in the short term, basing on registered data in previous time instants. A correct prediction could potentially be of interesting use in the design of smart power grids. In this study, several models and configurations have been compared to determine the best and most economical setup needed for registering data of the prediction. The study includes comparisons of several ways of arranging the temporal data and pre-processing it with unsupervised techniques and several regression models. The novel approach has been tested empirically with a real dataset of measurements registered along a complete year; obtaining good results in all the operating condition ranges.
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Acknowledgements
We would like to thank the ‘Instituto Enerxético de Galicia’ (INEGA) and ‘Parque Eólico Experimental de Sotavento’ (Sotavento Foundation) for their technical support on this work.
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Baruque, B., Jove, E., Porras, S., Calvo-Rolle, J.L. (2019). Study of Data Pre-processing for Short-Term Prediction of Heat Exchanger Behaviour Using Time Series. In: Pérez García, H., Sánchez González, L., Castejón Limas, M., Quintián Pardo, H., Corchado Rodríguez, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_4
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