mlsanityData to 14 Oct 2026

Safe Deep Reinforcement Learning for Building Heating Control and Demand-side Flexibility

arXiv 2604.16033 ↗First posted 17 Apr 2026eess.SY cs.AI cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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 model while maintaining occupant comfort, minimizing energy cost, and providing flexibility. To address safety concerns with reinforcement learning, particularly regarding compliance with flexibility requests, we propose a real-time adaptive safety-filter to ensure that the system operates within predefined constraints during demand-side flexibility provision. The proposed real-time adaptive safety filter guarantees full compliance with flexibility requests from system operators and improves energy and cost efficiency -- achieving up to 50% savings compared to a rule-based controller -- while outperforming a standalone deep reinforcement learning-based controller in energy and cost metrics, with only a slight increase in comfort temperature violations.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 35 references that are in the map. First posted on arXiv 17 Apr 2026.

Builds on

5 of 35 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Optimal Management of Grid-Interactive Efficient Buildings via Safe Reinforcement Learning12 Sep

Other references · 4

  1. 2023State-wise Safe Reinforcement Learning: A Surveycited by 55
  2. 2020A Review of Deep Reinforcement Learning for Smart Building Energy Managementcited by 38
  3. 2021Physically Consistent Neural Networks for building thermal modeling: theory and analysiscited by 17
  4. 2024Safe Reinforcement Learning Filter for Multicopter Collision-Free Tracking under disturbancescited by 1

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Close in wording, no citation either way

  1. 2026Safe Deep Reinforcement Learning for Energy-Efficient HVAC Control in Multi-Zone Residential Buildings
  2. 2024An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control
  3. 2026Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings
  4. 2024A Critical Review of Safe Reinforcement Learning Techniques in Smart Grid Applications
  5. 2025Continual Reinforcement Learning for HVAC Systems Control: Integrating Hypernetworks and Transfer Learning
  6. 2026Higher-Order Action Regularization in Deep Reinforcement Learning: From Continuous Control to Building Energy Management
  7. 2026PIRS: Physics-Informed Reward Shaping for SAC-Based Building Energy Management
  8. 2026Verifier-Based Reinforcement Fine-Tuning of Reasoning Models for Thermal Energy Storage Control