A Reinforcement Learning Approach to Home Energy Management for Modulating Heat Pumps and Photovoltaic Systems (Papers Track)

Lissy Langer (TU Berlin)

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Buildings Reinforcement Learning


Efficient sector coupling in residential buildings plays a key role in supporting the energy transition. In this study, we analyze the potential of using reinforcement learning (RL) to control a home energy management system. We conduct this study by modeling a representative building with a modulating air-sourced heat pump, a photovoltaic system, a battery, and thermal storage systems for floor heating and hot-water supply. In our numerical analysis, we benchmark our reinforcement learning results using DDPG with the optimal solution generated with model predictive control using a mixed-integer linear model under full information. Our input data, models, and the RL environment, developed using the Julia programming language, will be available in an open-source manner.

Recorded Talk (direct link)