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

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.

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

The paper discusses the development and implementation of a Deep Reinforcement Learning-based framework that advances conventional Home Energy Management Systems (HEMS). Current HEMS literature often disregards the dynamic nature of consumer comfort preferences, addressing comfort deviation from standard appliance settings with static weighting factors. As a remedy, the authors propose a multi-mode Deep Reinforcement Learning-based HEMS (DRL-HEMS) system centered around dynamic consumer-defined preferences, aiming to strengthen consumer engagement in Demand Response (DR) programs.

The DRL-HEMS model employs a model-free, single-agent deep reinforcement learning algorithm. This choice of algorithm allows the system to learn optimal strategies for managing household energy consumption without relying on explicit models of environmental dynamics. The study validating this model uses real-world data, including electricity price, ambient temperature, and appliance power consumption metrics, aggregated at 15-minute intervals. The DRL-HEMS framework introduces three distinct modes, each reflecting varying levels of flexibility for consumer engagement in DR programs. These modes range from maintaining default appliance settings to broadening the settings' flexibility, enabling significant cost savings without compromising comfort.

Key findings include the model's ability to achieve nearly optimal performance while considerably enhancing computational efficiency compared to traditional methods like Mixed-Integer Linear Programming (MILP). When compared against MILP, the DRL model reflected a minimal 2.5% difference in energy costs, showcasing its effectiveness in aligning closely with optimal solutions. The computational efficiency is particularly notable as the DRL-HEMS model necessitates only one second for decision-making processes, starkly outperforming MILP, which takes five seconds.

The scalability of the DRL-HEMS system is particularly promising, adaptable to various settings whether homes operate independently or in concert. For independent operations, transfer learning methodologies prove advantageous, permitting adjustments without requiring comprehensive retraining. In coordinated scenarios, employing decentralized MARL or federated learning can enhance scalability while ensuring system privacy and efficient processing.

Practically, this model is positioned as a robust tool for real-time energy management, particularly in environments subject to unpredictable energy costs and appliance demands. The integrated multi-mode preferences fortify the paradigm by prioritizing consumer autonomy, allowing settings to be modulated as needed for comfort or savings.

Future research directions could expand the model's application by integrating additional residential appliances and resources, such as renewable energy systems and storage solutions. Incorporating real-world performance variations—like battery degradation and efficiency loss—would further enhance model accuracy. Additionally, scaling the model to accommodate a broader network of homes and exploring the consequences of rebound effects would provide deeper insights into optimizing collective energy management with minimal disruption. The pursuit of these enhancements promises both theoretical and practical advancements in AI-driven smart home systems, leading to more efficient, flexible, and consumer-friendly energy solutions.