Zhao, Pengxiang, Dong, Zhao Yang, Meng, Ke, Kong, Weicong, and Yang, Jiajia (2021) Household power usage pattern filtering-based residential electricity plan recommender system. Applied Energy, 298. 117191.

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Abstract

Deregulation of the retail electricity market has led to the emergence of an increasing number of electricity plans with competitive rates. Electricity customers now have more flexibility in choosing an electricity provider and electricity plan based on individual consumption needs. In this paper, a feature engineering hybrid collaborative filtering-based electricity plan recommender system (FECF-EPRS) is proposed for helping the customer get the right electricity plan. This system is composed of three-segment models for missing feature estimation, feature crosses construction, and electricity plan recommendation. It only takes easy-to-obtain household appliance usage features as inputs and outputs ratings for different plans. Through the test of real electricity market data, the FECF-EPRS shows a greater improvement in terms of recommendation accuracy, which can provide more accurate recommendations to customers and more reasonable pricing references for retailers.

Item ID:	78139
Item Type:	Article (Research - C1)
ISSN:	1872-9118
Copyright Information:	© 2021 Published by Elsevier Ltd.
Date Deposited:	12 Apr 2023 03:33
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460502 Data mining and knowledge discovery @ 50% 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460501 Data engineering and data science @ 50%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2201 Communication technologies, systems and services > 220199 Communication technologies, systems and services not elsewhere classified @ 50% 17 ENERGY > 1703 Energy storage, distribution and supply > 170309 Smart grids @ 50%
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