Learning a Reward Function for User-Preferred Appliance Scheduling

TL;DR

This paper introduces an inverse reinforcement learning model that creates personalized appliance schedules based on user data, promoting demand response participation while respecting privacy and comfort.

Contribution

It presents a novel approach to infer user preferences for appliance scheduling without explicit input, enhancing demand response engagement.

Findings

Successfully infers user preferences from consumption data

Improves user participation in demand response programs

Respects user privacy and comfort

Abstract

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.