In the current era, electricity demand has skyrocketed. Power grids have to face a lot of uneven power demand daily. During a certain period in a day, the power demand peaks, making it difficult for the grid to meet the demand. To deal with this problem, an intelligent Home Energy Management (HEM) can be beneficial. Smart HEM systems can schedule loads from peak to low peak hours. Thereby reducing peak load on the grid as well as reducing decreasing the costs incurred by a user. In this paper, we proposed a Deep Reinforcement Learning model with prioritized experience sampling (PQDN-DR) for appropriate demand response, and the problem of load shifting is simulated as a game. We also propose a novel reward system for better convergence of the DRL model to near-optimal strategies and a DR adapted Epsilon Greedy Policy to guide the agent in exploration phase for faster convergence. The proposed system minimizes power demand peak and consumers’ bills simultaneously. The proposed method has successfully reduced the peak load and peak costs in smaller DR environment. The agent reduced costs and overall variance of the load profile for all customers for 24 h in the standard DR environment.

在当前时代，电力需求激增。电网每天必须面对许多不平衡的电力需求。在一天中的特定时间段内，电力需求达到峰值，从而使电网难以满足需求。为了解决此问题，智能家庭能源管理（HEM）可能会有所帮助。Smart HEM系统可以安排从高峰时段到低峰时段的负载。从而减少了电网上的峰值负载，并且减少了减少由用户引起的成本。在本文中，我们提出了具有优先经验采样（PQDN-DR）的深度强化学习模型，以进行适当的需求响应，并且将负荷转移问题作为游戏进行了仿真。我们还提出了一种新颖的奖励系统，以使DRL模型更好地收敛于近乎最优的策略，并提出了一种适应DR的Epsilon贪婪策略，以指导代理处于探索阶段以加快收敛速度​​。拟议的系统同时将电力需求高峰和用户账单减至最小。所提出的方法已成功降低了较小灾难恢复环境下的峰值负载和峰值成本。在标准灾难恢复环境中，该代理为所有客户减少了24小时的成本并降低了负载配置文件的总体差异。