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

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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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.
Original languageEnglish
Title of host publicationICIT2024
Subtitle of host publicationThe 2024 International Conference on Industrial Technology (ICIT)
Place of PublicationPiscataway, US
Number of pages6
ISBN (Electronic)9798350340266
ISBN (Print)9798350340273
Publication statusPublished - 25 Mar 2024
EventICIT2024: The 2024 International Conference on Industrial Technology (ICIT) - Bristol, United Kingdom
Duration: 25 Mar 202427 Mar 2024

Publication series

NameInternational Conference on Industrial Technology (ICIT)
ISSN (Print)2641-0184
ISSN (Electronic)2643-2978


Country/TerritoryUnited Kingdom
Internet address

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