Short⁃term electric power load forecasting based on accumulated temperature effect and optimized support vector machine

谭风雷; 陈梦涛; 汪龙龙

doi:10.3969/j.issn.1009-1831.2018.05.008

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Short⁃term electric power load forecasting based on accumulated temperature effect and optimized support vector machine

更新时间：2026-03-05

- Short⁃term electric power load forecasting based on accumulated temperature effect and optimized support vector machine
- Vol. 20, Issue 5, (2018)
- 作者机构：
  
  国网江苏省电力有限公司检修分公司,南京,211102
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2018.05.008
  CLC：
- Published：2018
- 稿件说明：
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谭风雷, 陈梦涛, 汪龙龙. Short⁃term electric power load forecasting based on accumulated temperature effect and optimized support vector machine[J]. 2018, 20(5).
DOI：

谭风雷, 陈梦涛, 汪龙龙. Short⁃term electric power load forecasting based on accumulated temperature effect and optimized support vector machine[J]. 2018, 20(5). DOI： 10.3969/j.issn.1009-1831.2018.05.008.

摘要

从积温效应的表现形式出发，提出一种考虑积温效应的优化支持向量机负荷预测方法。在充分研究积温效应2种表现形式的基础上，建立温度修正模型，同时为进一步提高预测精度，采用优化支持向量机预测负荷。最后借助江苏某地区的历史数据，采用最小二乘法求解积温效应参数、修正温度，并将修正之后的结果代入预测方法中，结果表明预测精度较高，具有一定的应用价值。

Abstract

The load forecast using optimized support vector machine was proposed based on accumulated temperature effect.The correction model of temperature was established based on study of two forms of accumulated temperature effects. In order to improve prediction accuracy

optimized support vector machine was adopted to forecast load. Finally

with the data of a region in Jiangsu as history data

least squares was adopted to obtain optimal parameters

and then the correction of temperature was used into the above method to forecast load. The results show high prediction accuracy and have good prospect.

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