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提供对于减少电力部门的碳排放至关重要。随着基础设施的进步，鼓励最终用户参与变得至关重要。最终用户高度重视其隐私和控制权，并希望在制定日常设备运行计划时参与服务设计和决策过程。此外，除非有经济或环境动机，他们通常不愿意牺牲舒适度来帮助平衡电力系统。在本文中，我们提出了一种基于逆向强化学习的模型，该模型有助于制定最终用户的日常设备计划，而无需他们明确陈述自身需求和意愿。通过利用其过去的消费数据，最终消费者将间接参与这些决策的制定，从而有动力继续参与需求响应服务的提供。