Dynamic Example Organization for Short-Term Load Forecasting

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Dynamic Example Organization for Short-Term Load Forecasting

更新时间：2025-07-09

- Dynamic Example Organization for Short-Term Load Forecasting
- Vol. 43, Issue 4, Pages: 95-100(2009)
- 作者机构：
  
  1. 西安交通大学电气工程学院,西安,710049
  2. 西北电网有限公司,西安,710048
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM715
- Online First：10 April 2009，
  
  Published：2009
- 稿件说明：
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Dynamic Example Organization for Short-Term Load Forecasting[J]. 2009, 43(4): 95-100.
DOI：

Dynamic Example Organization for Short-Term Load Forecasting[J]. 2009, 43(4): 95-100. DOI：

摘要

针对训练样本与负荷预测模型的构建及预测精度之间的强相关性

在对负荷变化规律深入研究的基础上

提出了样本动态组织理论与方法.根据负荷变化的横向与纵向特征、日期、季节特征和气象特征构建时间分类树和样本映射表

并通过对气象数据的模糊化处理进行样本初选

进而利用自组织网络(SOFM)的改进方法提取负荷水平变化趋势的特征曲线

以实现样本的动态精选.多种模型的预测结果表明

采用的由粗到精逐步细化

多层面、多角度的样本过滤机制

为预测日负荷建模提供了更加优质的历史样本

很好地抑制了不良样本对预测建模可能带来的各种干扰

有效提高了电力系统短期负荷预测精度.

Abstract

For the strong correlations among training examples

load forecasting model construction and forecasting accuracy

on the basis of researching into the load changing law

the theory and corresponding method for dynamic example organization were presented. According to the horizontal and longitudinal load changing characters

date

season and weather characters

the time classification tree and example map were constructed. Then

the examples were selected preliminarily via fuzzily dealing with weather data

and the dynamic selection of examples was completed via extracting the characteristic curve of load level changing trend by the improved self-organizing feature map(SOFM). The results of several forecasting models indicate the filtration mechanism with several levels and perspectives can offer better training examples to inhibit interference from bad examples and get higher forecasting accuracy of short-term load.

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references

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夏经德,夏道止.带温度补偿的神经网络用于电力系统短期负荷预测[J].西北电力技术,2006,34(3):1-3.

XIA Jingde, XIA Daozhi. Short-term load forecasting by neural network with temperature compensating [J]. Northwest China Electric Power, 2006,34(3):1-3.

赵登福,王蒙,张讲社,等.基于支撑向量机方法的短期负荷预测[J].中国电机工程学报,2002,22(4):26-30.

ZHAO Dengfu, WANG Meng, ZHANG Jiangshe, et al. A support vector machine approach for short term load forecasting[J]. Proceedings of the CSEE, 2002,22(4):26-30.

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