Name
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 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 domain, since it may facilitate the fit of a single model on more than one source domain (houses). This allows GOTL to transfer knowledge from more than one source domain. 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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.enbuild.2016.12.074 | Energy and Buildings |
Keywords | Field | DocType |
Climate control in buildings,Online transfer learning,Model predictive control | Convergence (routing),Data mining,Transfer of learning,Model predictive control,Comfort levels,Artificial intelligence,Engineering,Predictive modelling,Component analysis,Machine learning | Journal |
Volume | ISSN | Citations |
139 | 0378-7788 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Grubinger | 1 | 9 | 1.52 |
| Georgios C. Chasparis | 2 | 89 | 14.77 |
| Thomas Natschläger | 3 | 1 | 3.14 |