# Experimental Data-Driven Model Predictive Control of a Hospital HVAC System During Regular Use

arXiv: https://arxiv.org/abs/2112.07323 · HTML: https://mlsanity.com/paper/2112.07323/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2021.

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

- [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)
- [Exploring Artificial Intelligence Methods for Energy Prediction in Healthcare Facilities: An In-Depth Extended Systematic Review](https://mlsanity.com/paper/2311.15807/) (2023)
- [Improving Building Temperature Forecasting: A Data-driven Approach with System Scenario Clustering](https://mlsanity.com/paper/2402.13628/) (2024)
- [Q-DASC: State-of-the-Art Safe Quantum Control for HVAC under Local Model Misspecification](https://mlsanity.com/paper/2606.28834/) (2026)

## Abstract (arXiv)

Herein we report a multi-zone, heating, ventilation and air-conditioning (HVAC) control case study of an industrial plant responsible for cooling a hospital surgery center. The adopted approach to guaranteeing thermal comfort and reducing electrical energy consumption is based on a statistical non-parametric, non-linear regression technique named Gaussian processes. Our study aimed at assessing the suitability of the aforementioned technique to learning the building dynamics and yielding models for our model predictive control (MPC) scheme. Experimental results gathered while the building was under regular use showcase the final controller performance while subject to a number of measured and unmeasured disturbances. Finally, we provide readers with practical details and recommendations on how to manage the computational complexity of the on-line optimization problem and obtain high-quality solutions from solvers.