Study of Data Pre-processing for Short-Term Prediction of Heat Exchanger Behaviour Using Time Series

• Bruno Baruque ^[1] ; Esteban Jove ^[2] ; Santiago Porras ^[1] ; José Luis Calvo-Rolle ^[2]
  1. [1] Universidad de Burgos

     Universidad de Burgos

     Burgos, España

     
  2. [2] Universidade da Coruña

     Universidade da Coruña

     A Coruña, España

     
• Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2019, ISBN 978-3-030-29858-6, págs. 38-49
• Idioma: inglés
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• Resumen

  • 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 preprocessing 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.