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

• URL: http://arxiv.org/abs/2604.16033v1
• Date: Fri, 17 Apr 2026 13:03:02 GMT
• Title: Safe Deep Reinforcement Learning for Building Heating Control and Demand-side Flexibility
• Abstract summary: Buildings account for approximately 40% of global energy consumption.<n>This paper presents a safe deep reinforcement learning-based control framework to optimize building space heating.
• Score: 3.9714388855765344
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Shielded Controller Units for RL with Operational Constraints Applied to Remote Microgrids [50.64533198075622]
  Reinforcement learning (RL) is a powerful framework for optimizing decision-making in complex systems under uncertainty.<n>In this paper, we introduce Shielded Controller Units (SCUs), a systematic and interpretable approach that leverages prior knowledge of system dynamics.<n>We demonstrate the effectiveness of SCUs on a remote microgrid optimization task with strict operational requirements.
  arXiv  Detail & Related papers  (2025-11-30T19:28:34Z)
• Human-in-the-Loop AI for HVAC Management Enhancing Comfort and Energy Efficiency [10.01352946835079]
  We propose a Human-in-the-Loop (HITL) Artificial Intelligence framework that optimize HVAC performance by incorporating real-time user feedback and responding to electricity prices.<n>Unlike conventional systems that require predefined information about occupancy or comfort levels, our approach learns and adapts based on ongoing user input.<n>By integrating the occupancy prediction model with reinforcement learning, the system improves operational efficiency and reduces energy costs in line with electricity market dynamics.
  arXiv  Detail & Related papers  (2025-05-09T05:23:37Z)
• Graph-Enhanced Model-Free Reinforcement Learning Agents for Efficient Power Grid Topological Control [0.24578723416255752]
  This paper presents a novel approach within the model-free framework of reinforcement learning, aimed at optimizing power network operations without prior expert knowledge.<n>We demonstrate that our approach achieves a consistent reduction in power losses, while ensuring grid stability against potential blackouts.
  arXiv  Detail & Related papers  (2025-03-26T16:20:30Z)
• Explainable Reinforcement Learning-based Home Energy Management Systems using Differentiable Decision Trees [4.573008040057806]
  The residential sector is another major and largely untapped source of flexibility, driven by the increased adoption of solar PV, home batteries, and EVs. We introduce a reinforcement learning-based approach using differentiable decision trees. This approach integrates the scalability of data-driven reinforcement learning with the explainability of (differentiable) decision trees. As a proof-of-concept, we analyze our method using a home energy management problem, comparing its performance with commercially available rule-based baseline and standard neural network-based RL controllers.
  arXiv  Detail & Related papers  (2024-03-18T16:40:41Z)
• Empowering Aggregators with Practical Data-Driven Tools: Harnessing Aggregated and Disaggregated Flexibility for Demand Response [37.69303106863453]
  This study explores the interaction between aggregators and building occupants in activating flexibility through Demand Response (DR) programs.<n>It introduces a methodology of optimizing aggregated flexibility provision strategies in environments with limited data.
  arXiv  Detail & Related papers  (2024-01-19T14:43:04Z)
• Laxity-Aware Scalable Reinforcement Learning for HVAC Control [2.0625936401496237]
  We tackle the curse of dimensionality issue in modeling and control by utilizing the concept of laxity to quantify the emergency level of each operation request. We propose a two-level approach to address energy optimization for a large population of HVAC systems.
  arXiv  Detail & Related papers  (2023-06-29T01:28:14Z)
• Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework [53.97223237572147]
  This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users.
  arXiv  Detail & Related papers  (2022-11-29T01:18:58Z)
• Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization [57.88118988775461]
  We propose PROjected Feasibility (PROF) to enforce convex operational constraints within neural policies. We demonstrate PROF on two applications: energy-efficient building operation and inverter control.
  arXiv  Detail & Related papers  (2021-05-19T01:58:10Z)
• Closing the Closed-Loop Distribution Shift in Safe Imitation Learning [80.05727171757454]
  We treat safe optimization-based control strategies as experts in an imitation learning problem. We train a learned policy that can be cheaply evaluated at run-time and that provably satisfies the same safety guarantees as the expert.
  arXiv  Detail & Related papers  (2021-02-18T05:11:41Z)
• Learning Robust Hybrid Control Barrier Functions for Uncertain Systems [68.30783663518821]
  We propose robust hybrid control barrier functions as a means to synthesize control laws that ensure robust safety. Based on this notion, we formulate an optimization problem for learning robust hybrid control barrier functions from data. Our techniques allow us to safely expand the region of attraction of a compass gait walker that is subject to model uncertainty.
  arXiv  Detail & Related papers  (2021-01-16T17:53:35Z)
• NeurOpt: Neural network based optimization for building energy management and climate control [58.06411999767069]
  We propose a data-driven control algorithm based on neural networks to reduce this cost of model identification. We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy.
  arXiv  Detail & Related papers  (2020-01-22T00:51:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.