# Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/2205.15703 · HTML: https://mlsanity.com/paper/2205.15703/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 31 May 2022.

## Builds on (most cited first, 14 of 14)

- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Nonlinear System Identification: A User-Oriented Roadmap](https://mlsanity.com/paper/1902.00683/) (2019)
- [Data-Enabled Predictive Control: In the Shallows of the DeePC](https://mlsanity.com/paper/1811.05890/) (2018)
- [Data-Driven MPC for Quadrotors](https://mlsanity.com/paper/2102.05773/) (2021)
- [Identification of Gaussian Process State Space Models](https://mlsanity.com/paper/1705.10888/) (2017)
- [Physically Consistent Neural Networks for building thermal modeling: theory and analysis](https://mlsanity.com/paper/2112.03212/) (2021)
- [Localized active learning of Gaussian process state space models](https://mlsanity.com/paper/2005.02191/) (2020)
- [Data-driven control of room temperature and bidirectional EV charging using deep reinforcement learning: simulations and experiments](https://mlsanity.com/paper/2103.01886/) (2021)
- [Linear tracking MPC for nonlinear systems Part II: The data-driven case](https://mlsanity.com/paper/2105.08567/) (2021)
- [How Training Data Impacts Performance in Learning-based Control](https://mlsanity.com/paper/2005.12062/) (2020)
- [Nonlinear Data-Enabled Prediction and Control](https://mlsanity.com/paper/2101.03187/) (2021)
- [Experimental Data-Driven Model Predictive Control of a Hospital HVAC System During Regular Use](https://mlsanity.com/paper/2112.07323/) (2021)
- [Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control](https://mlsanity.com/paper/2203.05434/) (2022)
- [Adaptive Robust Data-driven Building Control via Bi-level Reformulation: an Experimental Result](https://mlsanity.com/paper/2106.05740/) (2021)

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

- [Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification](https://mlsanity.com/paper/2502.02133/) (2025)

## Abstract (arXiv)

This manuscript offers the perspective of experimentalists on a number of modern data-driven techniques: model predictive control relying on Gaussian processes, adaptive data-driven control based on behavioral theory, and deep reinforcement learning. These techniques are compared in terms of data requirements, ease of use, computational burden, and robustness in the context of real-world applications. Our remarks and observations stem from a number of experimental investigations carried out in the field of building control in diverse environments, from lecture halls and apartment spaces to a hospital surgery center. The final goal is to support others in identifying what technique is best suited to tackle their own problems.