# Energy-Efficient Thermal Comfort Control in Smart Buildings via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1901.04693 · HTML: https://mlsanity.com/paper/1901.04693/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2019.

## Built on by (most cited first, 8 of 8)

- [Semi-analytical Industrial Cooling System Model for Reinforcement Learning](https://mlsanity.com/paper/2207.13131/) (2022)
- [Distributed Multi-Agent Deep Reinforcement Learning Framework for Whole-building HVAC Control](https://mlsanity.com/paper/2110.13450/) (2021)
- [Optimizing Industrial HVAC Systems with Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2209.08112/) (2022)
- [Potential Impacts of Smart Homes on Human Behavior: A Reinforcement Learning Approach](https://mlsanity.com/paper/2102.13307/) (2021)
- [Intelligent Building Control Systems for Thermal Comfort and Energy-Efficiency: A Systematic Review of Artificial Intelligence-Assisted Techniques](https://mlsanity.com/paper/2104.02214/) (2021)
- [Autonomous Payload Thermal Control](https://mlsanity.com/paper/2307.15438/) (2023)
- [Optimizing Indoor Environmental Quality in Smart Buildings Using Deep Learning](https://mlsanity.com/paper/2509.26187/) (2025)
- [An Optimal Battery-Free Approach for Emission Reduction by Storing Solar Surplus in Building Thermal Mass](https://mlsanity.com/paper/2603.28217/) (2026)

## Abstract (arXiv)

Heating, Ventilation, and Air Conditioning (HVAC) is extremely energy-consuming, accounting for 40% of total building energy consumption. Therefore, it is crucial to design some energy-efficient building thermal control policies which can reduce the energy consumption of HVAC while maintaining the comfort of the occupants. However, implementing such a policy is challenging, because it involves various influencing factors in a building environment, which are usually hard to model and may be different from case to case. To address this challenge, we propose a deep reinforcement learning based framework for energy optimization and thermal comfort control in smart buildings. We formulate the building thermal control as a cost-minimization problem which jointly considers the energy consumption of HVAC and the thermal comfort of the occupants. To solve the problem, we first adopt a deep neural network based approach for predicting the occupants' thermal comfort, and then adopt Deep Deterministic Policy Gradients (DDPG) for learning the thermal control policy. To evaluate the performance, we implement a building thermal control simulation system and evaluate the performance under various settings. The experiment results show that our method can improve the thermal comfort prediction accuracy, and reduce the energy consumption of HVAC while improving the occupants' thermal comfort.