Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 15, Issue 8, Pages 4731-4741Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2019.2917318
Keywords
Bayesian probabilistic matrix factorization (BPMF); collaborative filtering (CF); electricity retailing plan; recommender system (RS); relevance support vector; residential customer
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Funding
- Joint Research Fund for Overseas Chinese Scholars in Hong Kong and Macao Young Scholars of NSFC [61728301]
- Australian Research Council [DP170103427]
- Australian Research Council Research Hub [IH180100020]
- UNSW Digital Grid Futures Institute, UNSW Sydney, under a cross disciplinary fund scheme
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The deregulation of the electricity market enables residential customers to select suitable electricity retailing plans. This paper proposes a Bayesian hybrid collaborative filtering-based electricity plan recommender system (BHCF-EPRS), which is constructed in a two-stage model integrated with model-based and memory-based collaborative filteringmethods. Bayesian inference is developed for missing feature estimation and user classification. Free from the requirements on total electricity use data and historical plan transaction data, the BHCF-EPRS can recommend suitable retailers and plans based on some easily obtainable features quantifying home appliance usage patterns. The BHCF-EPRS is verified to be a reliable recommender system with low error in full-ranking recommendation and high precision in top-N recommendation, which can improve the competitive operation of the electricity market.
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