# Optimal Management of Grid-Interactive Efficient Buildings via Safe Reinforcement Learning

arXiv: https://arxiv.org/abs/2409.08132 · HTML: https://mlsanity.com/paper/2409.08132/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 18 references that are in the map.
- First posted on arXiv 12 Sep 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 7 of 7)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Conservative Safety Critics for Exploration](https://mlsanity.com/paper/2010.14497/) (2020)
- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021)
- [Decentralized and Distributed Temperature Control via HVAC Systems in Energy Efficient Buildings](https://mlsanity.com/paper/1702.03308/) (2017)
- [Distributed Temperature Control via Geothermal Heat Pump Systems in Energy Efficient Buildings](https://mlsanity.com/paper/1702.08819/) (2017)
- [Two-Facet Scalable Cooperative Optimization of Multi-Agent Systems in The Networked Environment](https://mlsanity.com/paper/2010.06157/) (2020)
- [Two-Level Decentralized-Centralized Control of Distributed Energy Resources in Grid-Interactive Efficient Buildings](https://mlsanity.com/paper/2211.07926/) (2022)

## Built on by (most cited first, 1 of 1)

- [Safe Deep Reinforcement Learning for Building Heating Control and Demand-side Flexibility](https://mlsanity.com/paper/2604.16033/) (2026)

## Abstract (arXiv)

Reinforcement learning (RL)-based methods have achieved significant success in managing grid-interactive efficient buildings (GEBs). However, RL does not carry intrinsic guarantees of constraint satisfaction, which may lead to severe safety consequences. Besides, in GEB control applications, most existing safe RL approaches rely only on the regularisation parameters in neural networks or penalty of rewards, which often encounter challenges with parameter tuning and lead to catastrophic constraint violations. To provide enforced safety guarantees in controlling GEBs, this paper designs a physics-inspired safe RL method whose decision-making is enhanced through safe interaction with the environment. Different energy resources in GEBs are optimally managed to minimize energy costs and maximize customer comfort. The proposed approach can achieve strict constraint guarantees based on prior knowledge of a set of developed hard steady-state rules. Simulations on the optimal management of GEBs, including heating, ventilation, and air conditioning (HVAC), solar photovoltaics, and energy storage systems, demonstrate the effectiveness of the proposed approach.