# A Review of Deep Reinforcement Learning for Smart Building Energy Management

arXiv: https://arxiv.org/abs/2008.05074 · HTML: https://mlsanity.com/paper/2008.05074/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2020.

## Built on by (most cited first, 30 of 38)

- [PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning Algorithm](https://mlsanity.com/paper/2208.07914/) (2022)
- [Controlling Commercial Cooling Systems Using Reinforcement Learning](https://mlsanity.com/paper/2211.07357/) (2022)
- [Pareto Set Learning for Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2501.06773/) (2025)
- [Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey](https://mlsanity.com/paper/2504.21048/) (2025)
- [Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management](https://mlsanity.com/paper/2301.00641/) (2022)
- [State-Wise Safe Reinforcement Learning With Pixel Observations](https://mlsanity.com/paper/2311.02227/) (2023)
- [Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2302.00521/) (2023)
- [A Lightweight Calibrated Simulation Enabling Efficient Offline Learning for Optimal Control of Real Buildings](https://mlsanity.com/paper/2310.08569/) (2023)
- [The Smart Buildings Control Suite: A Diverse Open Source Benchmark to Evaluate and Scale HVAC Control Policies for Sustainability](https://mlsanity.com/paper/2410.03756/) (2024)
- [Foundation Models for CPS-IoT: Opportunities and Challenges](https://mlsanity.com/paper/2501.16368/) (2025)
- [An experimental evaluation of Deep Reinforcement Learning algorithms for HVAC control](https://mlsanity.com/paper/2401.05737/) (2024)
- [Low Emission Building Control with Zero-Shot Reinforcement Learning](https://mlsanity.com/paper/2208.06385/) (2022)
- [Decision-making with Speculative Opponent Models](https://mlsanity.com/paper/2211.11940/) (2022)
- [Creation of AI-driven Smart Spaces for Enhanced Indoor Environments -- A Survey](https://mlsanity.com/paper/2412.14708/) (2024)
- [Counter-Dyna: Data-Efficient RL-Based HVAC Control using Counterfactual Building Models](https://mlsanity.com/paper/2605.04555/) (2026)
- [Machine Learning for Smart and Energy-Efficient Buildings](https://mlsanity.com/paper/2211.14889/) (2022)
- [Energy Optimization for HVAC Systems in Multi-VAV Open Offices: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2306.13333/) (2023)
- [Data-Driven Policy Mapping for Safe RL-based Energy Management Systems](https://mlsanity.com/paper/2506.16352/) (2025)
- [Does Explicit Prediction Matter in Deep Reinforcement Learning-Based Energy Management?](https://mlsanity.com/paper/2108.05099/) (2021)
- [Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling under Data Governance](https://mlsanity.com/paper/2110.02784/) (2021)
- [Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2206.01663/) (2022)
- [Low Emission Building Control with Zero-Shot Reinforcement Learning](https://mlsanity.com/paper/2206.14191/) (2022)
- [Conv-NILM-Net, a causal and multi-appliance model for energy source separation](https://mlsanity.com/paper/2208.02173/) (2022)
- [The Benefit of Noise-Injection for Dynamic Gray-Box Model Creation](https://mlsanity.com/paper/2310.01517/) (2023)
- [Experimental evaluation of offline reinforcement learning for HVAC control in buildings](https://mlsanity.com/paper/2408.07986/) (2024)
- [Logic-Free Building Automation: Learning the Control of Room Facilities with Wall Switches and Ceiling Camera](https://mlsanity.com/paper/2410.02789/) (2024)
- [Recommender systems and reinforcement learning for human-building interaction and context-aware support: A text mining-driven review of scientific literature](https://mlsanity.com/paper/2411.08734/) (2024)
- [Decision-Focused Learning for Complex System Identification: HVAC Management System Application](https://mlsanity.com/paper/2501.14708/) (2025)
- [Physics-informed Modularized Neural Network for Advanced Building Control by Deep Reinforcement Learning](https://mlsanity.com/paper/2504.05397/) (2025)
- [Integration of Multi-Mode Preference into Home Energy Management System Using Deep Reinforcement Learning](https://mlsanity.com/paper/2505.01332/) (2025)

## Abstract (arXiv)

Global buildings account for about 30% of the total energy consumption and carbon emission, raising severe energy and environmental concerns. Therefore, it is significant and urgent to develop novel smart building energy management (SBEM) technologies for the advance of energy-efficient and green buildings. However, it is a nontrivial task due to the following challenges. Firstly, it is generally difficult to develop an explicit building thermal dynamics model that is both accurate and efficient enough for building control. Secondly, there are many uncertain system parameters (e.g., renewable generation output, outdoor temperature, and the number of occupants). Thirdly, there are many spatially and temporally coupled operational constraints. Fourthly, building energy optimization problems can not be solved in real-time by traditional methods when they have extremely large solution spaces. Fifthly, traditional building energy management methods have respective applicable premises, which means that they have low versatility when confronted with varying building environments. With the rapid development of Internet of Things technology and computation capability, artificial intelligence technology find its significant competence in control and optimization. As a general artificial intelligence technology, deep reinforcement learning (DRL) is promising to address the above challenges. Notably, the recent years have seen the surge of DRL for SBEM. However, there lacks a systematic overview of different DRL methods for SBEM. To fill the gap, this paper provides a comprehensive review of DRL for SBEM from the perspective of system scale. In particular, we identify the existing unresolved issues and point out possible future research directions.