mlsanityData to 14 Oct 2026

Integration of Multi-Mode Preference into Home Energy Management System Using Deep Reinforcement Learning

arXiv 2505.01332 ↗First posted 2 May 2025cs.LG cs.SY eess.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Home Energy Management Systems (HEMS) have emerged as a pivotal tool in the smart home ecosystem, aiming to enhance energy efficiency, reduce costs, and improve user comfort. By enabling intelligent control and optimization of household energy consumption, HEMS plays a significant role in bridging the gap between consumer needs and energy utility objectives. However, much of the existing literature construes consumer comfort as a mere deviation from the standard appliance settings. Such deviations are typically incorporated into optimization objectives via static weighting factors. These factors often overlook the dynamic nature of consumer behaviors and preferences. Addressing this oversight, our paper introduces a multi-mode Deep Reinforcement Learning-based HEMS (DRL-HEMS) framework, meticulously designed to optimize based on dynamic, consumer-defined preferences. Our primary goal is to augment consumer involvement in Demand Response (DR) programs by embedding dynamic multi-mode preferences tailored to individual appliances. In this study, we leverage a model-free, single-agent DRL algorithm to deliver a HEMS framework that is not only dynamic but also user-friendly. To validate its efficacy, we employed real-world data at 15-minute intervals, including metrics such as electricity price, ambient temperature, and appliances' power consumption. Our results show that the model performs exceptionally well in optimizing energy consumption within different preference modes. Furthermore, when compared to traditional algorithms based on Mixed-Integer Linear Programming (MILP), our model achieves nearly optimal performance while outperforming in computational efficiency.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 41 references that are in the map. First posted on arXiv 2 May 2025.

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6 of 41 references.

References · 6

  1. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  2. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  3. 2019Challenges of Real-World Reinforcement Learningcited by 291
  4. 2020A Review of Deep Reinforcement Learning for Smart Building Energy Managementcited by 38
  5. 2019Deep Reinforcement Learning for Smart Home Energy Managementcited by 30
  6. 2018Deep Reinforcement Learning for Optimal Control of Space Heatingcited by 7

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  8. 2024Inferring Preferences from Demonstrations in Multi-Objective Residential Energy Management