AI Framework Optimizes Building Heating for Energy Grid Flexibility
A research paper introduces a safe deep reinforcement learning control framework for building heating systems to provide demand-side flexibility for power grids. Buildings consume about 40% of global energy, making heating, ventilation, and air conditioning systems critical for grid stability as renewable energy sources grow. The framework uses a deep deterministic policy gradient algorithm to learn optimal heating strategies through interaction with a building thermal model. It aims to maintain occupant comfort, minimize energy costs, and enable flexibility provision for power system operators. Safety concerns with reinforcement learning, especially regarding compliance with flexibility requests, are addressed with a proposed real-time adaptive safety filter. The paper, arXiv:2604.16033v1, was announced as a cross-disciplinary abstract, highlighting the intersection of AI technology and energy efficiency in building operations.
Key facts
- Buildings account for approximately 40% of global energy consumption
- The paper presents a safe deep reinforcement learning-based control framework for building space heating
- A deep deterministic policy gradient algorithm is used as the core method
- The controller learns optimal heating strategies through interaction with a building thermal model
- Goals include maintaining occupant comfort, minimizing energy cost, and providing flexibility
- Demand-side flexibility in heating is essential for grid stability with intermittent renewable energy
- Safety concerns with reinforcement learning are addressed with a real-time adaptive safety filter
- The paper is arXiv:2604.16033v1 and announced as cross
Entities
Institutions
- arXiv