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Personalized Residential Energy Usage Recommendation System Based on Load Monitoring and Collaborative Filtering

handle: 1959.4/unsworks_68131
Residential demand response (DR) is recognized as a promising approach to improve grid energy efficiency and relieve the network stress. Many studies have been conducted to design home energy management systems that directly schedule and control the household appliances. Distinguished from existing works, this article proposes a personalized recommendation system (PRS) to learn energy-efficient household appliance usage experiences from a large scale of residential users, and recommends suitable appliance usage plans to users while taking their lifestyles into account. The proposed system is based on a collaborative filtering recommendation technique. The PRS first classifies a collection of users as “highly responsive users” and “less responsive users” based on their DR degree analysis. Then, for each less responsive user, the PRS infers the user's lifestyle from usage profiles of nonshiftable appliances and finds out users who have similar habits with the target user from the set of highly responsive users. Based on this, the PRS evaluates the lifestyle similarity between the target user and each smart user, aggregates the appliance usage experiences of highly responsive users, and makes appliance-use recommendations to the target user. Experiments based on a residential data simulator “SimHouse” are designed to validate the proposed system.
- University of Sydney Australia
- Chinese University of Hong Kong China (People's Republic of)
- UNSW Sydney Australia
4608 Human-Centred Computing, Clinical Trials and Supportive Activities, anzsrc-for: 4605 Data Management and Data Science, anzsrc-for: 46 Information and Computing Sciences, 004, 4605 Data Management and Data Science, anzsrc-for: 40 Engineering, anzsrc-for: 4608 Human-Centred Computing, 46 Information and Computing Sciences, Clinical Research, 7 Affordable and Clean Energy, anzsrc-for: 09 Engineering, anzsrc-for: 08 Information and Computing Sciences, anzsrc-for: 10 Technology, 40 Engineering
4608 Human-Centred Computing, Clinical Trials and Supportive Activities, anzsrc-for: 4605 Data Management and Data Science, anzsrc-for: 46 Information and Computing Sciences, 004, 4605 Data Management and Data Science, anzsrc-for: 40 Engineering, anzsrc-for: 4608 Human-Centred Computing, 46 Information and Computing Sciences, Clinical Research, 7 Affordable and Clean Energy, anzsrc-for: 09 Engineering, anzsrc-for: 08 Information and Computing Sciences, anzsrc-for: 10 Technology, 40 Engineering
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).36 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
