在智能家居中使用自适应强化学习技术进行动态设备调度和能源管理
Poonam Saroha1, Gopal Singh1, Umesh Kumar Lilhore2
1Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, Haryana, India.
本研究介绍了使用自适应式Puma优化算法 (SAPOA) 和多目标深度Q网络 (MO-DQN) 的智能家居能源管理系统. 它显著降低了峰值与平均值比率 (PAR),以提高能源效率和节省成本.
科学领域:
- 智能家居的能源管理
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 传统的家庭能源管理系统与动态的用户偏好和成本作斗争.
- 现有的强化学习方法往往缺乏高级优化集成.
研究的目的:
- 为智能家居开发一种新的需求响应 (DR) 方法.
- 改善能源消耗,成本管理和用户偏好适应.
- 通过智能设备调度来提高能源效率.
主要方法:
- 自适应式Puma优化算法 (SAPOA) 与多目标深度Q网络 (MO-DQN) 的集成.
- SAPOA通过适应性最大化了多个目标;MO-DQN通过交互学习增强了决策.
- 利用过去的能源使用模式进行偏好调整和设备调度优化.
主要成果:
- 极大降低了峰值与平均值比率 (PAR),从3.4286降至1.9765 (没有 RES) 和1.0339 (有 RES).
- 与MORL-POA,SAPOA和POA方法相比,已经证明了更高的性能.
- 有效管理不确定性,提高整体系统性能和灵活性.
结论:
- 拟议的SAPOA-MO-DQN方法为智能家居能源管理提供了灵活和高性能的解决方案.
- 有效地优化设备调度和能源使用,同时适应用户偏好.
- 显著降低能源成本,并通过减少峰值负载提高电网稳定性.
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