Collaborative Filtering-based Electricity Plan Recommender System
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This paper presents a collaborative filtering-based electricity plan recommender system (EPRS) that helps residential customers choose suitable electricity plans based on their household appliance data. The EPRS demonstrates improved accuracy in recommendations compared to other strategies, offering valuable guidance in the selection of plans and tariffs. The document outlines the hardware and software requirements for the system's implementation.
Collaborative Filtering-based Electricity Plan Recommender System
- 1. Collaborative Filtering-based Electricity Plan Recommender System ABSTRACT: Owning to electricity market deregulation, residential customers now enjoy the freedom to choose their preferred electricity retailers. This paper investigates the application of recommender system, a fast-developing technique in machine learning, into the task of recommending electricity plans for the individual residential customer. Based on a collaborative filtering strategy, an electricity plan recommender system (EPRS) is developed. By providing easily obtainable data of some household appliances, residential customers of the EPRS are recommended with predicted ratings of different plans, which can provide effective guidance to customers in the selection of suitable plans and proper tariffs. Different numerical tests are carried out to evalu-ate the performance of the EPRS. The EPRS outperforms other strategies in the accuracy of recommendation result and is verified to be a promising solution to electricity plan recommendation task. SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS: System : Pentium Dual Core. Hard Disk : 120 GB. Monitor : 15’’ LED Input Devices : Keyboard, Mouse Ram : 1 GB
- 2. SOFTWARE REQUIREMENTS: Operating system : Windows 7. Coding Language : Python Database : MYSQL REFERENCE: Yuan Zhang, Student Member, IEEE, Ke Meng, Member, IEEE, Weicong Kong, Member, IEEE and Zhao Yang Dong, Fellow, IEEE, “Collaborative Filtering- based Electricity Plan Recommender System”, IEEE Transactions on Industrial Informatics, 2019.