Application of Fuzzy Support Vector Machine in Short-Term Power Load Forecasting

被引：3

作者：

Yang, Jie ^{[1
]}

Tang, Yachun ^{[1
]}

Duan, Huabin ^{[1
]}

机构：

[1] Hunan Univ Sci & Engn, Coll Informat Engn, Yongzhou, Peoples R China

来源：

JOURNAL OF CASES ON INFORMATION TECHNOLOGY | 2022年 / 24卷 / 05期

关键词：

Fuzzy Support Vector Machine; Linear Extrapolatio; Load Forecasting; Power System; Short-Term Forecast of Power;

D O I：

10.4018/JCIT.295248

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The realization of short-term load forecasting is the basis of system planning and decision-making, and it is an important index to evaluate the safety and economy of power grid. In order to accurately predict the power load under the influence of many factors, a new short-term power load prediction method based on fuzzy support vector machine and similar daily linear extrapolation is proposed, which combines the method of fuzzy support vector machine and linear extrapolation of similar days. The method first selects similar days according to the effect of integrated weather and time on load. Then the fuzzy membership of the training sample is obtained by the normalization processing, and the daily maximum and minimum load is predicted by the fuzzy support vector machine. Finally, the load prediction value is obtained by combining the load trend curve obtained by the similar daily linear extrapolation method, and this method is feasible and effective for short-term forecasting of power load.

引用

页数：10

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