# Adaptive Robust Data-driven Building Control via Bi-level Reformulation: an Experimental Result

arXiv: https://arxiv.org/abs/2106.05740 · HTML: https://mlsanity.com/paper/2106.05740/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2021.

## Built on by (most cited first, 2 of 2)

- [Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A review](https://mlsanity.com/paper/2503.05022/) (2025)
- [Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning](https://mlsanity.com/paper/2205.15703/) (2022)

## Abstract (arXiv)

Data-driven control approaches for the minimization of energy consumption of buildings have the potential to significantly reduce deployment costs and increase uptake of advanced control in this sector. A number of recent approaches based on the application of Willems' fundamental lemma for data-driven controller design from input/output measurements are very promising for deterministic LTI systems. This paper changeproposes a systematic way to handle unknown measurement noise and measurable process noise, and extends these data-driven control schemes to adaptive building control via a robust bi-level formulation, whose upper level ensures robustness and whose lower level guarantees prediction quality. Corresponding numerical improvements and an active excitation mechanism are proposed to enable a computationally efficient reliable operation. The efficacy of the proposed scheme is validated by changea multi-zone building simulation and a real-world experiment on a single-zone conference building on the EPFL campus. The real-world experiment includes a 20-day non-stop test, where, without extra modeling effort, our proposed controller improves 18.4% energy efficiency against an industry-standard controller, while also robustly ensuring occupant comfort.