Forecasting of bus.bar net load based on PSR.ARIMA and SVR

金山红; 朱想; 赫卫国; 王波; 梅飞; 李玉杰; 袁晓玲; 刘皓明

doi:10.3969/j.issn.1009-1831.2018.02.005

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Forecasting of bus.bar net load based on PSR.ARIMA and SVR

更新时间：2026-03-05

- Forecasting of bus.bar net load based on PSR.ARIMA and SVR
- Vol. 20, Issue 2, (2018)
- 作者机构：
  
  1. 国网浙江省电力公司嘉兴供电公司,浙江,嘉兴,314000
  2. 中国电力科学研究院,南京,210003
  3. 国网浙江省电力公司宁波电力公司,浙江,宁波,315000
  4. 河海大学能源与电气学院,南京,211100
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2018.02.005
  CLC：
- Published：2018
- 稿件说明：
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金山红, 朱想, 赫卫国, et al. Forecasting of bus.bar net load based on PSR.ARIMA and SVR[J]. 2018, 20(2).
DOI：

金山红, 朱想, 赫卫国, et al. Forecasting of bus.bar net load based on PSR.ARIMA and SVR[J]. 2018, 20(2). DOI： 10.3969/j.issn.1009-1831.2018.02.005.

摘要

实现对含分布式电源母线净负荷的实时跟踪和预测，以分布式光伏并入电网的母线负荷情况为基础，根据净负荷本身固有的线性和非线性属性，提出了基于相空间重构自回归滑动平均( autoregressive moving arerage

ARIMA)和支持向量机( support vector machine

SVR)的母线净负荷预测方法。首先基于历史净负荷数据使用 ARIMA建立拟合模型，对净负荷线性成分预测分析，之后用 CC法对非线性成分进行相空间重构，利用 SVR模型对非线性部分进行预测。数据建模的结果表明，提出的 ARIMA.CC_SVR预测模型对含有分布式光伏成分的母线净负荷适用性较强。

Abstract

In order to realize the real.time tracking and fore.casting of net load with distributed power bus

in the light of the busload with distributed photovoltaic power grid

according to the linearand nonlinear properties of net load inherent

the autoregressivemoving average with the phase space reconstruction and support vec.tor machine bus.bar net load forecasting method is proposed.Firstly

based on the historical net load data

the ARIMA model is estab.lished to forecast the linear components of the net load.Then

thephase space of the nonlinear components is reconstructed by CCmethod.Finally

the nonlinear part is forecasted by the SVR model.The results of data modeling show that the proposed ARIMA .CC_SVR forecasting model is suitable for the net load of the bus.barwith distributed PV components.

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