Inferring Preferences from Demonstrations in Multi-Objective Residential Energy Management

• URL: http://arxiv.org/abs/2401.07722v1
• Date: Mon, 15 Jan 2024 14:36:59 GMT
• Title: Inferring Preferences from Demonstrations in Multi-Objective Residential Energy Management
• Authors: Junlin Lu, Patrick Mannion, Karl Mason
• Abstract summary: 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. 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.
• Score: 3.354345524478023
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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