TY - GEN
T1 - Big Data-driven Electricity Plan Recommender System
AU - Zhang, Yuan
AU - Kong, Weicong
AU - Dong, Zhao Yang
AU - Meng, Ke
AU - Qiu, Jin
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2018/12/21
Y1 - 2018/12/21
N2 - The deregulation of electricity retailing market enables residential customers to select suitable electricity retailing plans to lower energy expenditures. This paper proposes a hybrid collaborative filtering-based electricity plan recommender system (HCF-EPRS), which is constructed in a two-stage model integrated with model-based and memory-based collaborative filtering skills. A weighted similarity metric is developed for a better similarity evaluation. Free from the requirements on whole house electricity usage data and historical plan transactions data, residential customers can obtain effective instructions in retailer and plan selection from HCF-EPRS through supplying some easily obtainable features. These features are the weekly operation duration times of some common household appliances. Through numerical tests on practical electricity users and retailing plans, HCF-EPRS is verified outperforming other approaches in recommending accuracy. Ideally, the instructions of HCF-EPRS on electricity retailer and plan selection helps to improve the competitive operation of the electricity market. © 2018 IEEE.
AB - The deregulation of electricity retailing market enables residential customers to select suitable electricity retailing plans to lower energy expenditures. This paper proposes a hybrid collaborative filtering-based electricity plan recommender system (HCF-EPRS), which is constructed in a two-stage model integrated with model-based and memory-based collaborative filtering skills. A weighted similarity metric is developed for a better similarity evaluation. Free from the requirements on whole house electricity usage data and historical plan transactions data, residential customers can obtain effective instructions in retailer and plan selection from HCF-EPRS through supplying some easily obtainable features. These features are the weekly operation duration times of some common household appliances. Through numerical tests on practical electricity users and retailing plans, HCF-EPRS is verified outperforming other approaches in recommending accuracy. Ideally, the instructions of HCF-EPRS on electricity retailer and plan selection helps to improve the competitive operation of the electricity market. © 2018 IEEE.
KW - Data informatics
KW - Electricity retailing plan
KW - Recommender system
UR - https://www.scopus.com/pages/publications/85060789634
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85060789634&origin=recordpage
U2 - 10.1109/PESGM.2018.8585885
DO - 10.1109/PESGM.2018.8585885
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781538677032
VL - 2018-August
T3 - IEEE Power and Energy Society General Meeting
BT - 2018 IEEE Power and Energy Society General Meeting, PESGM 2018
PB - IEEE Computer Society
T2 - 2018 IEEE Power and Energy Society General Meeting, PESGM 2018
Y2 - 5 August 2018 through 10 August 2018
ER -