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

Home Energy Management Systems (HEMS) have emerged as a pivotal tool in the smart home ecosystem, aiming to enhance energy efficiency, reduce costs, and im- prove user comfort. By enabling intelligent control and optimization of household energy consumption, HEMS plays a significant role in bridging the gap between con- sumer 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 optimiza- tion 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 Re- inforcement Learning-based HEMS (DRL-HEMS) frame- work, meticulously designed to optimize based on dy- namic, consumer-defined preferences. Our primary goal is to augment consumer involvement in Demand Response (DR) programs by embedding dynamic multi-mode pref- erences 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 ap- pliances' 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 outperform- ing in computational efficiency.