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

• URL: http://arxiv.org/abs/2505.01332v1
• Date: Fri, 02 May 2025 15:05:29 GMT
• Title: Integration of Multi-Mode Preference into Home Energy Management System Using Deep Reinforcement Learning
• Authors: Mohammed Sumayli, Olugbenga Moses Anubi,
• Abstract summary: Home Energy Management Systems (HEMS) have emerged as a pivotal tool in the smart home ecosystem.<n>This paper introduces a multi-mode Deep Reinforcement Learning-based HEMS framework, meticulously designed to optimize based on dynamic, consumer-defined preferences.<n>Our results show that the model performs exceptionally well in optimizing energy consumption within different preference modes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Green MLOps to Green GenOps: An Empirical Study of Energy Consumption in Discriminative and Generative AI Operations [2.2765705959685234]
  This study investigates the energy consumption of Discriminative and Generative AI models within real-world MLOps pipelines.<n>We employ software-based power measurements to ensure ease of replication across diverse configurations, models, and datasets.
  arXiv  Detail & Related papers  (2025-03-31T10:28:04Z)
• COSMOS: A Hybrid Adaptive Optimizer for Memory-Efficient Training of LLMs [81.01082659623552]
  Large Language Models (LLMs) have demonstrated remarkable success across various domains.<n>Their optimization remains a significant challenge due to the complex and high-dimensional loss landscapes they inhabit.
  arXiv  Detail & Related papers  (2025-02-24T18:42:19Z)
• Advancing Generative Artificial Intelligence and Large Language Models for Demand Side Management with Internet of Electric Vehicles [52.43886862287498]
  This paper explores the integration of large language models (LLMs) into energy management.<n>We propose an innovative solution that enhances LLMs with retrieval-augmented generation for automatic problem formulation, code generation, and customizing optimization.<n>We present a case study to demonstrate the effectiveness of our proposed solution in charging scheduling and optimization for electric vehicles.
  arXiv  Detail & Related papers  (2025-01-26T14:31:03Z)
• BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT [2.6872737601772956]
  In Mobile Edge IoT (MEIoT), the training and communication processes can significantly deplete the limited battery resources of devices.<n>We propose BEFL, a joint optimization framework aimed at balancing three objectives: enhancing global model accuracy, minimizing total energy consumption, and reducing energy usage disparities among devices.<n>Our experiments reveal that BEFL improves global model accuracy by 1.6%, reduces energy consumption variance by 72.7%, and lowers total energy consumption by 28.2% compared to existing methods.
  arXiv  Detail & Related papers  (2024-12-05T07:58:32Z)
• An LLM-Based Digital Twin for Optimizing Human-in-the Loop Systems [13.388869442538399]
  We present a case study that employs large language models (LLMs) to mimic the behaviors and thermal preferences of various population groups in a shopping mall. The aggregated thermal preferences are integrated into an agent-in-the-loop based reinforcement learning algorithm AitL-RL. Our results show that LLMs are capable of simulating complex population movements within large open spaces.
  arXiv  Detail & Related papers  (2024-03-25T14:32:28Z)
• Inferring Preferences from Demonstrations in Multi-Objective Residential Energy Management [3.354345524478023]
  Demonstration-based preference inference (DemoPI) is a promising approach to mitigate this problem. Understanding the behaviours and values of energy customers is an example of a scenario where preference inference can be used. In this work, we applied the state-of-art DemoPI method, i.e., the dynamic weight-based preference inference (DWPI) algorithm in a multi-objective residential energy consumption setting.
  arXiv  Detail & Related papers  (2024-01-15T14:36:59Z)
• A Human-on-the-Loop Optimization Autoformalism Approach for Sustainability [27.70596933019959]
  This paper outlines a natural conversational approach to solving personalized energy-related problems using large language models (LLMs) We put forward a strategy that augments an LLM with an optimization solver, enhancing its proficiency in understanding and responding to user specifications and preferences. Our approach pioneers the novel concept of human-guided optimization autoformalism, translating a natural language task specification automatically into an optimization instance.
  arXiv  Detail & Related papers  (2023-08-20T22:42:04Z)
• Learning Implicit Priors for Motion Optimization [105.11889448885226]
  Energy-based Models (EBM) represent expressive probability density distributions. We present a set of required modeling and algorithmic choices to adapt EBMs into motion optimization.
  arXiv  Detail & Related papers  (2022-04-11T19:14:54Z)
• Automated Machine Learning: A Case Study on Non-Intrusive Appliance Load Monitoring [81.06807079998117]
  We propose a novel approach to enable Automated Machine Learning (AutoML) for Non-Intrusive Appliance Load Monitoring (NIALM)<n>NIALM offers a cost-effective alternative to smart meters for measuring the energy consumption of electric devices and appliances.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems [87.4519172058185]
  An effective energy dispatch mechanism for self-powered wireless networks with edge computing capabilities is studied. A novel multi-agent meta-reinforcement learning (MAMRL) framework is proposed to solve the formulated problem. Experimental results show that the proposed MAMRL model can reduce up to 11% non-renewable energy usage and by 22.4% the energy cost.
  arXiv  Detail & Related papers  (2020-02-20T04:58:07Z)
• NeurOpt: Neural network based optimization for building energy management and climate control [58.06411999767069]
  We propose a data-driven control algorithm based on neural networks to reduce this cost of model identification. We validate our learning and control algorithms on a two-story building with ten independently controlled zones, located in Italy.
  arXiv  Detail & Related papers  (2020-01-22T00:51:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.