XGBoost regression model-based electricity tariff plan recommendation in smart grid environment. (3rd June 2022)

• Record Type: Journal Article
• Authors: Behera, Dayal Kumar; Das, Madhabananda; Swetanisha, Subhra; Nayak, Janmenjoy
• Is Part Of: International journal of innovative computing and applications. Volume 13:Number 2(2022)
• Page Start: 79
• Page End: 87
• Publication Date: 2022-06-03
• Subjects: XGBoost regression model -- recommender system -- smart grid -- power system -- electricity tariff plan recommendation; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

Power system deregulation enables the power industry to provide residential customers to choose retailing electricity plan. This allows competition among retailers or traders and also minimises the energy expenditure with quality of services. We have proposed an XGBoost regression model for electricity tariff plan recommendation. Firstly, proposed regression model with basic statistical features is compared with support vector regression (SVR), decision tree (DT), Bayesian ridge and KNN regression model. Secondly, performance of the proposed model is extensively studied by combining the features from other user-based, item-based and matrix factorisation-based techniques. In this research, dataset shared in the project Smart Grid Smart City (SGSC), Australia is used for conducting experimental analysis. A rating inference approach is designed to infer the choice of electricity consumer for a specific retailing plan. The proposed model achieves better performance as compared to other baseline methods.