Inferring Preferences from Demonstrations in Multi-Objective Residential Energy Management
This work addresses the problem of intuitive preference specification for energy customers, though it appears incremental as it applies an existing method to a new domain.
The paper tackled the challenge of users articulating preferences in multi-objective residential energy management by applying the dynamic weight-based preference inference (DWPI) algorithm to infer preferences from demonstrations, achieving accurate results in three scenarios.
It is often challenging for a user to articulate their preferences accurately in multi-objective decision-making problems. Demonstration-based preference inference (DemoPI) is a promising approach to mitigate this problem. Understanding the behaviours and values of energy customers is an example of a scenario where preference inference can be used to gain insights into the values of energy customers with multiple objectives, e.g. cost and comfort. In this work, we applied the state-of-art DemoPI method, i.e., the dynamic weight-based preference inference (DWPI) algorithm in a multi-objective residential energy consumption setting to infer preferences from energy consumption demonstrations by simulated users following a rule-based approach. According to our experimental results, the DWPI model achieves accurate demonstration-based preference inferring in three scenarios. These advancements enhance the usability and effectiveness of multi-objective reinforcement learning (MORL) in energy management, enabling more intuitive and user-friendly preference specifications, and opening the door for DWPI to be applied in real-world settings.