RESEARCH ARTICLE


Application of Support Vector Regression and Time Series Method in Short-term Power Load Forecasting with Regional Difference



Li-Ling Peng1, Song-Qiao Dong1, Meng Yu1, Guo-Feng Fan1, *, Wei-Chiang Hong2
1 School of Mathematics & Statistics, Ping Ding Shan University, Ping Ding Shan 467000, Henan, China;
2 School of Education Intelligent Technology, Jiangsu Normal University/101, Shanghai Rd., Tongshan District, Xuzhou 221116, Jiangsu, China

Article Information


Identifiers and Pagination:

Year: 2021
Volume: 1
Issue: 1
E-location ID: e190721194078
Publisher ID: e190721194078
DOI: 10.2174/2666782701666210614223415

Article History:

Received Date: 23/03/2021
Revision Received Date: 22/05/2021
Acceptance Date: 26/05/2021
Electronic publication date: 14/06/2021

Article Metrics

CrossRef Citations:
1
Total Statistics:

Full-Text HTML Views: 155
Abstract HTML Views: 106
PDF Downloads: 132
Total Views/Downloads: 480
Unique Statistics:

Full-Text HTML Views: 114
Abstract HTML Views: 89
PDF Downloads: 104
Total Views/Downloads: 376



Creative Commons License
Copyright: 2022 Bentham Science Publishers

* Address correspondence to this author at the School of Mathematics & Statistics, Ping Ding Shan University, Ping Ding Shan 467000, Henan, China; E-mail: guofengtongzhi@163.com


Abstract

Aim: The aim of this study is to perform short-term load forecasting.

Background: Short-term load forecasting plays a key role in power dispatching. It provides basic data for basic power generation planning and system safety analysis so that the power dispatching work is more practical and the power generation efficiency is higher.

Objective: The aim of this study is to ensure the safe operation of the electricity market and relieve the pressure of supply and demand.

Methods: In this paper, the SVR model is used for short-term load prediction.

Results: The SVR model has the advantage of minimizing the structural risk and has good generalization performance for the predicted object. At the same time, the global optimization is ensured, a lot of mapping calculation is reduced, the actual risk is reduced, and the prediction performance is improved.

Conclusion: The target model has higher forecasting accuracy than other forecasting models and can effectively solve the problems of the power market.

Keywords: Short-term load forecasting, support vector regression (SVR), empirical mode decomposition (EMD), ARIMA, regional difference, intelligent algorithm.