基于AdaBoost-KELM方法的短期电力负荷预测研究

《无线互联科技》杂志社 ›› 2022, Vol. 19 ›› Issue (9): 134-136.

基于AdaBoost-KELM方法的短期电力负荷预测研究

任瑞琪

西安铁路职业技术学院,陕西西安 710026

出版日期:2022-05-10 发布日期:2022-07-25
作者简介:任瑞琪(1994— ),女,陕西西安人,助教,硕士;研究方向:电力负荷预测风电功率预测。
基金资助:
项目名称:改进核极限学习机方法在智能电网中的应用;项目编号:XTZY21G06。

Research on short-term power load forecasting based on Ada Boost-KELM method

Ren Ruiqi

School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Online:2022-05-10 Published:2022-07-25

摘要/Abstract

摘要： 针对短期电力负荷预测,在极限学习机的基础上,提出了一种AdaBoost-KELM的方法。文章将KELM作为AdaBoost方法的基学习器,将AdaBoost-KELM方法应用于某地区的单步或者多步的短期电力负荷预测的实例中,在同等条件下,与BP,RBF,ELM,KELM,AdaBoost-BP,AdaBoost-RBF,AdaBoost-ELM几种方法进行比较。实验结果表明,所提出的AdaBoost-KELM方法在预测精度上最有优势。

关键词: 电力负荷预测, 极限, 学习机

Abstract: A method of Adaboost-KELM is proposed based on the limit learning machine.This article will KELM AdaBoost method of learning, as the AdaBoost-KELM method was applied to single or more steps in an area of the short-term power load forecasting example, under the same condition, with BP, RBF, ELM, KELM, AdaBoost-BP, AdaBoost-RBF, AdaBoost-ELM comparing several methods. The experimental results show that the proposed AdaBoost-KELM method is the most advantageous in predicting accuracy.

Key words: prediction of short-term power load, kernel extreme, learning machine

任瑞琪. 基于AdaBoost-KELM方法的短期电力负荷预测研究[J]. 《无线互联科技》杂志社, 2022, 19(9): 134-136.

Ren Ruiqi. Research on short-term power load forecasting based on Ada Boost-KELM method[J]. Wireless Internet Technology Magazine Agency, 2022, 19(9): 134-136.

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