Accurate Action Recommendations and Demand Response for Smart Homes via Knowledge Graphs

Chen, Wenzhi; Sun, Hongjian; You, Minglei; Jiang, Jing

Abstract

Accurate action recommendations can enhance the convenience of daily life, such as automatically turning on the dining area lights during meals or playing music based on residential habits. Generating precise recommendations for the next household device actions is essential for future smart homes. This paper proposes an action recommendation system for household appliance scenarios by customizing the knowledge graph attention network in its sampling and aggregation, in which the usage habits, periods, and location information were used as common sense for graph modelling. The results of the recommendations can be explained by a designed method with the trained embeddings. Finally, with the recommendation expectation, appliances' comfort level and average power are modelled as a multi-objective optimization problem for participating in demand response. Simulations demonstrate that the proposed system can achieve 93.4% accuracy in recommendations and reduce the power consumption by 20% while providing reasonable explanations.

Citation

Chen, W., Sun, H., You, M., & Jiang, J. (2024, March). Accurate Action Recommendations and Demand Response for Smart Homes via Knowledge Graphs. Presented at The 2024 International Conference on Industrial Technology (ICIT), Bristol, UK

• Presentation Conference Type: Edited Proceedings
• Conference Name: The 2024 International Conference on Industrial Technology (ICIT)
• Start Date: Mar 25, 2024
• End Date: Mar 27, 2024
• Acceptance Date: Mar 25, 2024
• Online Publication Date: Jun 5, 2024
• Publication Date: Mar 25, 2024
• Deposit Date: Jun 24, 2024
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Pages: 1--6
• Series ISSN: 2643-2978
• Book Title: 2024 IEEE International Conference on Industrial Technology (ICIT)
• ISBN: 979-8-3503-4027-3
• DOI: https://doi.org/10.1109/ICIT58233.2024.10540733
• Public URL: https://nottingham-repository.worktribe.com/output/36294333
• Publisher URL: https://ieeexplore.ieee.org/document/10540733