# Trajectory tracking with an aggregation of domestic hot water heaters: Combining model-based and model-free control in a commercial deployment

arXiv: https://arxiv.org/abs/1805.04228 · HTML: https://mlsanity.com/paper/1805.04228/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2018.

## Built on by (most cited first, 3 of 3)

- [Physics Informed Neural Networks for Control Oriented Thermal Modeling of Buildings](https://mlsanity.com/paper/2111.12066/) (2021)
- [Combined Peak Reduction and Self-Consumption Using Proximal Policy Optimization](https://mlsanity.com/paper/2211.14831/) (2022)
- [Real-World Implementation of Reinforcement Learning Based Energy Coordination for a Cluster of Households](https://mlsanity.com/paper/2310.19155/) (2023)

## Abstract (arXiv)

Scalable demand response of residential electric loads has been a timely research topic in recent years. The commercial coming of age or residential demand response requires a scalable control architecture that is both efficient and practical to use. This work presents such a strategy for domestic hot water heaters and present a commercial proof-of-concept deployment. The strategy combines state of the art in aggregate-and-dispatch with a novel dispatch strategy leveraging recent developments in reinforcement learning and is tested in a hardware-in-the-loop simulation environment. The results are promising and present how model-based and model-free control strategies can be merged to obtain a mature and commercially viable control strategy for residential demand response.