# Experimental Demonstration of Frequency Regulation by Commercial Buildings - Part I: Modeling and Hierarchical Control Design

arXiv: https://arxiv.org/abs/1605.05835 · HTML: https://mlsanity.com/paper/1605.05835/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2016.

## Built on by (most cited first, 4 of 4)

- [Physics Informed Neural Networks for Control Oriented Thermal Modeling of Buildings](https://mlsanity.com/paper/2111.12066/) (2021)
- [A Survey on Physics Informed Reinforcement Learning: Review and Open Problems](https://mlsanity.com/paper/2309.01909/) (2023)
- [Demand response for residential building heating: Effective Monte Carlo Tree Search control based on physics-informed neural networks](https://mlsanity.com/paper/2312.03365/) (2023)
- [Uncertainty-Cognizant Model Predictive Control for Energy Management of Residential Buildings with PVT and Thermal Energy Storage](https://mlsanity.com/paper/2201.08909/) (2022)

## Abstract (arXiv)

This paper is the first part of a two-part series in which we present results from an experimental demonstration of frequency regulation in a commercial building test facility. In Part I, we introduce the test facility and develop relevant building models. Furthermore, we design a hierarchical controller that consists of three levels: a reserve scheduler, a building climate controller, and a fan speed controller for frequency regulation. We formulate the reserve scheduler as a robust optimization problem and introduce several approximations to reduce its complexity. The building climate controller is comprised of a robust model predictive controller and a Kalman filter. The frequency regulation controller consists of a feedback and a feedforward loop, provides fast responses, and is stable. Part I presents building model identification and controller tuning results, whereas Part II reports results from the operation of the hierarchical controller under frequency regulation.