Accelerated development of demand response service provision by the residential sector is crucial for reducing carbon-emissions in the power sector. Along with the infrastructure advancement, encouraging the end users to participate is crucial. End users highly value their privacy and control, and want to be included in the service design and decision-making process when creating the daily appliance operation schedules. Furthermore, unless they are financially or environmentally motivated, they are generally not prepared to sacrifice their comfort to help balance the power system. In this paper, we present an inverse-reinforcement-learning-based model that helps create the end users' daily appliance schedules without asking them to explicitly state their needs and wishes. By using their past consumption data, the end consumers will implicitly participate in the creation of those decisions and will thus be motivated to continue participating in the provision of demand response services.

翻译：居民部门加速发展需求响应服务对于减少电力部门碳排放至关重要。在基础设施升级的同时，激励终端用户参与至关重要。终端用户高度重视隐私和自主控制权，在制定日常家电运行调度方案时，希望被纳入服务设计和决策过程。此外，除非出于经济或环境动机，他们通常不愿牺牲舒适度来帮助平衡电力系统。本文提出一种基于逆向强化学习的模型，该模型无需用户明确陈述其需求与意愿，即可生成终端用户的日常家电调度方案。借助用户的过往用电数据，终端消费者将间接参与这些决策的生成过程，从而保持参与需求响应服务的积极性。