User-centric recommendations on energy-efficient appliances in smart grids : A Multi-task learning approach

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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Original languageEnglish
Article number111219
Journal / PublicationKnowledge-Based Systems
Online published22 Nov 2023
Publication statusPublished - 25 Jan 2024
Externally publishedYes



Deploying energy-efficient appliances is one of the most effective ways to save energy bills for residents. However, the existing recommender systems for energy-efficient appliances passively rely on energy consumption patterns without the knowledge of users’ true needs. This paper proposes a user-centric energy-efficient appliance personalized recommender system (EEA-PRS) based on information collected from load monitoring platforms and e-commerce websites. The proposed system is built in a novel multi-task learning approach to collaboratively infer user's preference on: (1) common types of appliances that appear in historical data; (2) energy-efficient models of common appliances; and (3) types of appliances that are novel to the users. The proposed system provides supervisory recommendation services with user feedback preferences on appliances as data labeling, which enables closed-loop evaluation to adhere to users’ needs and interests. Simulation studies with comparative analysis have been conducted to validate its leading recommendation performance in terms of conforming to user preferences. © 2023 The Author(s).

Research Area(s)

  • Collaborative filtering, Energy-efficient appliances, Multi-task learning, Recommender systems, Smart grid

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