The rising demand for electricity and its essential nature in today's world call for intelligent home energy management systems that can reduce energy usage. This article aims a novel way to develop a learning system that can learn from experience to shift loads from one time instance to another and achieve the goal of minimizing the aggregate peak load. Specifically, this article proposes a deep reinforcement learning model for demand response where the virtual agent learns the task like humans learns a task. The agent gets feedback for every action it takes in the environment; these feedbacks will drive the agent to learn about the environment and take much smarter steps later in its learning stages. The proposed approach outperformed the state of the art mixed integer linear programming for load peak reduction. Other key contribution is the design of an agent to minimize both consumer electricity bills and system peak load demand simultaneously. The proposed model was analyzed with loads from five different residential consumers; the method increases the monthly savings of each consumer by reducing their electricity bill drastically along with minimizing the peak load of the grid.

中文翻译：

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基于深度强化学习的智能住宅能源管理系统

电力需求的增长及其在当今世界中的基本性质要求可减少能源使用的智能家庭能源管理系统。本文旨在开发一种新颖的方法来开发一种学习系统，该系统可以从经验中学习将负载从一个时间实例转移到另一个实例，并实现最小化总峰值负载的目标。具体来说，本文提出了一种针对需求响应的深度强化学习模型，其中虚拟代理学习任务就像人类学习任务一样。代理会针对其在环境中采取的每项操作获取反馈；这些反馈将推动代理了解环境，并在其学习阶段的后期采取更明智的步骤。所提出的方法在降低负载峰值方面优于现有的混合整数线性规划技术。其他关键作用是设计代理，以同时最小化消费电费和系统峰值负载需求。所提出的模型是根据来自五个不同住宅用户的负载进行分析的；该方法通过大幅度降低他们的电费并最大程度地降低电网的峰值负荷，增加了每个用户的每月储蓄。