# Deep Reinforcement Learning for Smart Home Energy Management

arXiv: https://arxiv.org/abs/1909.10165 · HTML: https://mlsanity.com/paper/1909.10165/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2019.

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

- [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021)
- [Physically Consistent Neural Networks for building thermal modeling: theory and analysis](https://mlsanity.com/paper/2112.03212/) (2021)
- [Contingency-constrained economic dispatch with safe reinforcement learning](https://mlsanity.com/paper/2205.06212/) (2022)
- [Knowledge Transfer from Simple to Complex: A Safe and Efficient Reinforcement Learning Framework for Autonomous Driving Decision-Making](https://mlsanity.com/paper/2410.14468/) (2024)
- [Katakomba: Tools and Benchmarks for Data-Driven NetHack](https://mlsanity.com/paper/2306.08772/) (2023)
- [Go-Explore for Residential Energy Management](https://mlsanity.com/paper/2401.07710/) (2024)
- [A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learning](https://mlsanity.com/paper/2403.09499/) (2024)
- [Correlated Deep Q-learning based Microgrid Energy Management](https://mlsanity.com/paper/2103.04152/) (2021)
- [AutoFR: Automated Filter Rule Generation for Adblocking](https://mlsanity.com/paper/2202.12872/) (2022)
- [PBRE: A Rule Extraction Method from Trained Neural Networks Designed for Smart Home Services](https://mlsanity.com/paper/2207.08814/) (2022)
- [Machine Learning for Smart and Energy-Efficient Buildings](https://mlsanity.com/paper/2211.14889/) (2022)
- [Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network](https://mlsanity.com/paper/2307.01622/) (2023)
- [Edge AI for Internet of Energy: Challenges and Perspectives](https://mlsanity.com/paper/2311.16851/) (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)
- [TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithm](https://mlsanity.com/paper/2304.08310/) (2023)
- [Deep Reinforcement Learning for Artificial Upwelling Energy Management](https://mlsanity.com/paper/2308.10199/) (2023)
- [Learning a Reward Function for User-Preferred Appliance Scheduling](https://mlsanity.com/paper/2310.07389/) (2023)
- [A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimization](https://mlsanity.com/paper/2407.01653/) (2024)
- [Integration of Multi-Mode Preference into Home Energy Management System Using Deep Reinforcement Learning](https://mlsanity.com/paper/2505.01332/) (2025)
- [HMPC-assisted Adversarial Inverse Reinforcement Learning for Smart Home Energy Management](https://mlsanity.com/paper/2506.00898/) (2025)
- [BitRL-Light: 1-bit LLM Agents with Deep Reinforcement Learning for Energy-Efficient Smart Home Lighting Optimization](https://mlsanity.com/paper/2512.20623/) (2025)
- [Forecast Aware Deep Reinforcement Learning for Efficient Electricity Load Scheduling in Dairy Farms](https://mlsanity.com/paper/2601.08052/) (2026)
- [Reinforcement Learning-based Home Energy Management with Heterogeneous Batteries and Stochastic EV Behaviour](https://mlsanity.com/paper/2602.04578/) (2026)
- [Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources](https://mlsanity.com/paper/2606.24947/) (2026)
- [Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systems](https://mlsanity.com/paper/2607.10309/) (2026)
- [From Thermal Preference Prediction to Adaptive Thermal Intervention: A Reinforcement Learning Approach Using Physiological and Environmental Sensing](https://mlsanity.com/paper/2608.20423/) (2026)
- [Energy Vision--Language--Action: A Controlled Multimodal Benchmark for Intent-Conditioned Residential Energy Management](https://mlsanity.com/paper/2609.31648/) (2026)

## Abstract (arXiv)

In this paper, we investigate an energy cost minimization problem for a smart home in the absence of a building thermal dynamics model with the consideration of a comfortable temperature range. Due to the existence of model uncertainty, parameter uncertainty (e.g., renewable generation output, non-shiftable power demand, outdoor temperature, and electricity price) and temporally-coupled operational constraints, it is very challenging to determine the optimal energy management strategy for scheduling Heating, Ventilation, and Air Conditioning (HVAC) systems and energy storage systems in the smart home. To address the challenge, we first formulate the above problem as a Markov decision process, and then propose an energy management strategy based on Deep Deterministic Policy Gradients (DDPG). It is worth mentioning that the proposed strategy does not require the prior knowledge of uncertain parameters and building thermal dynamics model. Simulation results based on real-world traces demonstrate the effectiveness and robustness of the proposed strategy.