Generalized online transfer learning for climate control in residential buildings

Autoren Thomas Grubinger
Georgios C. Chasparis
Thomas Natschläger
Editoren
Titel Generalized online transfer learning for climate control in residential buildings
Typ Artikel
Journal Energy and Buildings
Band 139
DOI 10.1016/j.enbuild.2016.12.074
Monat March
Jahr 2017
Seiten 63-71
SCCH ID# 16084
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-predictivecontrol implementation), this paper introduces the 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. Furthermore, the use of Transfer Component Analysis (TCA) allows for using more than a single source domains, since it may facilitate the fit of a single model on more than one source domains (houses). This allows GOTL to transfer knowledge from more than one source domains. We further validate our results through experiments in climate control for residential buildings and show that GOTL may lead to non-negligible energy savings for given comfort levels.