内变量参数辨识的灰色电价预测模型

雷鸣雳 1; 2; 冯祖仁 1; 2

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内变量参数辨识的灰色电价预测模型

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- 内变量参数辨识的灰色电价预测模型
- A Grey Model to Predict Electricity Price with Internal Variable Parameters Identification
- 西安交通大学学报 2009年43卷第8期页码：85-89
- 作者机构：
  
  1. 西安交通大学系统工程研究所,西安,710049
  2. 西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
  
  : 2009-01-12. 作者简介: 雷鸣雳(1971-),男,博士生;冯祖仁(联系人),男,教授,博士生导师. 基金项目: 国家自然科学基金资助项目(60875043);国家重点基础研究发展规划资助项目(2007CB311006)
- DOI：
  中图分类号： TM74
- 网络首发：2009-08-10，
  
  纸质出版：2009
- 稿件说明：
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雷鸣雳 1, 2, 冯祖仁 1, 等. 内变量参数辨识的灰色电价预测模型[J]. 西安交通大学学报, 2009,43(8):85-89.

A Grey Model to Predict Electricity Price with Internal Variable Parameters Identification[J]. 2009, 43(8): 85-89.
雷鸣雳 1, 2, 冯祖仁 1, 等. 内变量参数辨识的灰色电价预测模型[J]. 西安交通大学学报, 2009,43(8):85-89. DOI：

A Grey Model to Predict Electricity Price with Internal Variable Parameters Identification[J]. 2009, 43(8): 85-89. DOI：

摘要

为了改善传统的电价预测灰色模型GM(1

1)的预测精度

提出一种内变量参数辨识的电价预测模型——PSOGM(1

1)模型.首先采用灰色微分方程建立模型内变量(发展系数、灰作用量、背景值权重系数、边值)与预测值之间的非线性内涵表达式

然后采用粒子群算法(PSO)对内变量参数进行辨识

得到问题的最优解

建立PSOGM(1

1)模型.与GM(1

1)模型相比较

PSOGM(1

1)模型具有较快的收敛速度和更好的预测精度.对北欧NORDPOOL电力市场历史电价数据的分析实验表明

PSOGM(1

1)模型的短期电价平均预测精度为94%

较已有的几种典型改进GM(1

1)模型预测精度提高了1%～3%.

Abstract

A PSOGM(1

1)model is proposed with internal variable parameters identification to improve the precision of the traditional GM(1

1)model for electricity price forecasting in power markets. The intensional expressions that describe the nonlinear relations between internal variable parameters(such as developing coefficients

the grey inputs

the background weight parameters

and the boundary-values)and forecasting values are deduced. Then the particle swarm optimization algorithm(PSO)is adopted to identify the internal parameters. The optimal solution to the model is obtained and the PSOGM(1

1)model is generated. Comparisons with the traditional GM(1

1)model show that the PSOGM(1

1)model provides faster convergence rate and better prediction precision. Numerical results on the historical data of NORDPOOL power market show that the average precision of the PSOGM(1

1)model is 94% for short-term price forecasting

and is 1% to 3% higher than the traditional GM(1

1)model and other typical improved GM(1

1)models.

关键词

Keywords

references

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DU Songhuai, HOU Zhijian, JIANG Chuanwen.A new short-term grey forecasting procedure of spot price [J].Journal of Grey System, 2002, 14(4):351-358.

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