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Home energy management system (HEMS) enables residents to actively participate in demand response (DR) programs. It can autonomously optimize the electricity usage of home appliances to reduce the electricity cost based on time-varying electricity prices. However, due to the existence of randomness in the pricing process of the utility and resident's activities, developing an efficient HEMS is challenging. To address this issue, we propose a novel home energy management method for optimal scheduling of different kinds of home appliances based on deep reinforcement learning (DRL). Specifically, we formulate the home energy management problem as an MDP considering the randomness of real-time electricity prices and resident's activities. A DRL approach based on proximal policy optimization (PPO) is developed to determine the optimal DR scheduling strategy. The proposed approach does not need any information on the appliances' models and distribution knowledge of the randomness. Simulation results verify the effectiveness of our proposed approach.
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This content will become publicly available on October 26, 2026
Home Energy Management for Uninterruptible Operation of Electricity-Dependent Durable Medical Equipment
Those who rely on electricity-dependent durable medical equipment (DME) often struggle to use their medical devices during prolonged power outages. With the increasing frequency of natural disasters and the growing use of electricity-dependent home medical devices, in addition to the continued integration of home-level renewable energy and mobile storage systems such as electric vehicles with vehicle-to-home (V2H) capabilities, home energy management systems (HEMS) must prioritize life-essential medical loads during extended power outages. This work integrates electricity-dependent DME into home energy management optimization. An oxygen concentrator and a hemodialysis machine are used as examples of medical devices with high power demands and distinct usage patterns. The HEMS model is formulated as a mixed-integer linear program (MILP) to minimize the total weighted load curtailment and thermal discomfort during extended outage scenarios. The results demonstrate that the HEMS is effective in sustaining DME operation.
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- PAR ID:
- 10656337
- Publisher / Repository:
- IEEE
- Date Published:
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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