# Building Model Identification during Regular Operation - Empirical Results and Challenges

arXiv: https://arxiv.org/abs/1603.06872 · HTML: https://mlsanity.com/paper/1603.06872/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2016.

## Built on by (most cited first, 1 of 1)

- [Neural Parameter Estimation of RC Thermal Building Models for Model Predictive Control](https://mlsanity.com/paper/2604.05904/) (2026)

## Abstract (arXiv)

The inter-temporal consumption flexibility of commercial buildings can be harnessed to improve the energy efficiency of buildings, or to provide ancillary service to the power grid. To do so, a predictive model of the building's thermal dynamics is required. In this paper, we identify a physics-based model of a multi-purpose commercial building including its heating, ventilation and air conditioning system during regular operation. We present our empirical results and show that large uncertainties in internal heat gains, due to occupancy and equipment, present several challenges in utilizing the building model for long-term prediction. In addition, we show that by learning these uncertain loads online and dynamically updating the building model, prediction accuracy is improved significantly.