Work

A novel MDPSO-SVR hybrid model for feature selection in electricity consumption forecasting

Year: 2022

Type: preprint

Abstract: Electricity consumption forecasting has vital importance for the energy planning of a country. Of the enabling machine learning models, support vector regression (SVR) has been widely used to set up forecasting models due to its superior generalization for unseen data. However, one key procedure for the predictive modeling is feature selection, which might hurt the prediction accuracy if improper features were selected. In this regard, a modified discrete particle swarm optimization (MDPSO) was employed for feature selection in this study, and then MDPSO-SVR hybrid mode was built to predict future electricity consumption. Compared with other well-established counterparts, MDPSO-SVR model consistently performs best in two real-world electricity consumption datasets, which indicates that MDPSO for feature selection can improve the prediction accuracy and the SVR equipped with the MDPSO can be a promised alternative for electricity consumption forecasting.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2206.06658

Authors: Bao, Yukun, Shen, Liang, Zhang, Xiaoyuan, Huang, Yanmei, Deng, Changrui

Institutions: Jiangxi University of Software Professional Technology, Jiangxi University of Engineering, Huazhong University of Science and Technology

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: green

Funder: National Natural Science Foundation of China