# Using Reinforcement Learning for Demand Response of Domestic Hot Water Buffers: a Real-Life Demonstration

arXiv: https://arxiv.org/abs/1703.05486 · HTML: https://mlsanity.com/paper/1703.05486/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2017.

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

- [Combined Peak Reduction and Self-Consumption Using Proximal Policy Optimization](https://mlsanity.com/paper/2211.14831/) (2022)
- [A Comparison of Model-Free and Model Predictive Control for Price Responsive Water Heaters](https://mlsanity.com/paper/2111.04689/) (2021)
- [Deadline-Aware, Energy-Efficient Control of Domestic Immersion Hot Water Heater](https://mlsanity.com/paper/2601.18123/) (2026)

## Abstract (arXiv)

This paper demonstrates a data-driven control approach for demand response in real-life residential buildings. The objective is to optimally schedule the heating cycles of the Domestic Hot Water (DHW) buffer to maximize the self-consumption of the local photovoltaic (PV) production. A model-based reinforcement learning technique is used to tackle the underlying sequential decision-making problem. The proposed algorithm learns the stochastic occupant behavior, predicts the PV production and takes into account the dynamics of the system. A real-life experiment with six residential buildings is performed using this algorithm. The results show that the self-consumption of the PV production is significantly increased, compared to the default thermostat control.