# Generalized Online Transfer Learning for Climate Control in Residential Buildings

arXiv: https://arxiv.org/abs/1610.04042 · HTML: https://mlsanity.com/paper/1610.04042/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2016.

## Built on by (most cited first, 5 of 5)

- [GenTL: A General Transfer Learning Model for Building Thermal Dynamics](https://mlsanity.com/paper/2501.13703/) (2025)
- [Adapting to Change: A Comparison of Continual and Transfer Learning for Modeling Building Thermal Dynamics under Concept Drifts](https://mlsanity.com/paper/2508.21615/) (2025)
- [Flusion: Integrating multiple data sources for accurate influenza predictions](https://mlsanity.com/paper/2407.19054/) (2024)
- [A Transfer Learning Approach to Minimize Reinforcement Learning Risks in Energy Optimization for Smart Buildings](https://mlsanity.com/paper/2305.00365/) (2023)
- [A Systems Theoretic Approach to Online Machine Learning](https://mlsanity.com/paper/2404.03775/) (2024)

## Abstract (arXiv)

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 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.