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The Chinese Journal of Artificial Intelligence

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ISSN (Print): 2666-7827
ISSN (Online): 2666-7835

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Research Article

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

Author(s): Li-Ling Peng, Song-Qiao Dong, Meng Yu, Guo-Feng Fan* and Wei-Chiang Hong

Volume 1, Issue 1, 2022

Published on: 14 June, 2021

Article ID: e190721194078 Pages: 13

DOI: 10.2174/2666782701666210614223415

Abstract

Aims: 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.

Graphical Abstract

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