Online transfer learning for climate control in residential buildings

Authors Thomas Grubinger
Georgios C. Chasparis
Thomas Natschläger
Editors
Title Online transfer learning for climate control in residential buildings
Booktitle Proceedings of the 5th Annual European Control Conference (ECC'16)
Type in proceedings
Publisher EUCA
ISBN 978-1-5090-2590-9
Month June
Year 2016
Pages 1183-1188
SCCH ID# 1574
Abstract

This paper presents an online transfer learning framework for improving temperature predictions in residential buildings. In transfer learning, prediction models trained under a set of available data from a target domain (e.g., house with limited data) can be improved through the use of data generated from similar source domains (e.g., houses with rich data). Given also the need for prediction models that can be trained online (e.g., as part of a model-predictive-control implementation), this paper introduces generalized online transfer learning algorithm (GOTL). It employs a weighted combination of the available predictors (i.e., the target and source predictors) and guarantees convergence to the best weighted predictor. We further validate our results through experiments in climate control for residential buildings.