Using Reinforcement Learning for Demand Response of Domestic Hot Water Buffers: a Real-Life Demonstration
This work addresses energy efficiency and cost reduction for residential building occupants, but it is incremental as it applies an existing reinforcement learning method to a specific real-world scenario.
The paper tackled the problem of optimizing domestic hot water heating cycles to increase self-consumption of local photovoltaic production in residential buildings, achieving a significant increase in self-consumption compared to default thermostat control in a real-life experiment with six buildings.
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.