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Deep Reinforcement Learning for Smart Home Energy Management

arXiv 1909.10165 ↗First posted 23 Sep 2019eess.SY cs.SYcited by 28

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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.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Sep 2019.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 6; 2024: 4; 2025: 4; 2026: 4.

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  1. 2021Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challengescited by 40
  2. 2021Physically Consistent Neural Networks for building thermal modeling: theory and analysiscited by 16
  3. 2022Contingency-constrained economic dispatch with safe reinforcement learningcited by 5
  4. 2023Katakomba: Tools and Benchmarks for Data-Driven NetHackcited by 5
  5. 2024Knowledge Transfer from Simple to Complex: A Safe and Efficient Reinforcement Learning Framework for Autonomous Driving Decision-Makingcited by 5
  6. 2024Go-Explore for Residential Energy Managementcited by 3
  7. 2024A Reinforcement Learning Approach to Dairy Farm Battery Management using Q Learningcited by 3
  8. 2021Correlated Deep Q-learning based Microgrid Energy Managementcited by 2
  9. 2022AutoFR: Automated Filter Rule Generation for Adblockingcited by 2
  10. 2022PBRE: A Rule Extraction Method from Trained Neural Networks Designed for Smart Home Servicescited by 2
  11. 2022Machine Learning for Smart and Energy-Efficient Buildingscited by 1
  12. 2023Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Networkcited by 1
16 more
  1. 2023Edge AI for Internet of Energy: Challenges and Perspectivescited by 1
  2. 2025Data-Driven Policy Mapping for Safe RL-based Energy Management Systemscited by 1
  3. 2021Does Explicit Prediction Matter in Deep Reinforcement Learning-Based Energy Management?cited by 0
  4. 2021Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling under Data Governancecited by 0
  5. 2022Joint Energy Dispatch and Unit Commitment in Microgrids Based on Deep Reinforcement Learningcited by 0
  6. 2023TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithmcited by 0
  7. 2023Deep Reinforcement Learning for Artificial Upwelling Energy Managementcited by 0
  8. 2023Learning a Reward Function for User-Preferred Appliance Schedulingcited by 0
  9. 2024A Deep Reinforcement Learning Approach to Battery Management in Dairy Farming via Proximal Policy Optimizationcited by 0
  10. 2025Integration of Multi-Mode Preference into Home Energy Management System Using Deep Reinforcement Learningcited by 0
  11. 2025HMPC-assisted Adversarial Inverse Reinforcement Learning for Smart Home Energy Managementcited by 0
  12. 2025BitRL-Light: 1-bit LLM Agents with Deep Reinforcement Learning for Energy-Efficient Smart Home Lighting Optimizationcited by 0
  13. 2026Forecast Aware Deep Reinforcement Learning for Efficient Electricity Load Scheduling in Dairy Farmscited by 0
  14. 2026Supervised Reinforcement Learning for the Coordination of Distributed Energy Resourcescited by 0
  15. 2026Measure the Sim-to-Real Gap: Designing an Affordable Real-World Benchmark Platform for Reinforcement Learning in AIoT Systemscited by 0
  16. 2026From Thermal Preference Prediction to Adaptive Thermal Intervention: A Reinforcement Learning Approach Using Physiological and Environmental Sensingcited by 0