Safe Deep Reinforcement Learning for Building Heating Control and Demand-side Flexibility
Buildings account for approximately 40% of global energy consumption, and with the growing share of intermittent renewable energy sources, enabling demand-side flexibility, particularly in heating, ventilation and air conditioning systems, is essential for grid stability and energy efficiency. This paper presents a safe deep reinforcement learning-based control framework to optimize building space heating while enabling demand-side flexibility provision for power system operators. A deep deterministic policy gradient algorithm is used as the core deep reinforcement learning method, enabling the controller to learn an optimal heating strategy through interaction with the building thermal m...
| Authors | Colin Jüni, Mina Montazeri, Yi Guo et al. |
|---|---|
| Primary category | eess.SY |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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