A Grey Model to Predict Electricity Price with Internal Variable Parameters Identification

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A Grey Model to Predict Electricity Price with Internal Variable Parameters Identification

更新时间：2025-07-09

- A Grey Model to Predict Electricity Price with Internal Variable Parameters Identification
- Vol. 43, Issue 8, Pages: 85-89(2009)
- 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：
- DOI：
  CLC： TM74
- Online First：10 August 2009，
  
  Published：2009
- 稿件说明：
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A Grey Model to Predict Electricity Price with Internal Variable Parameters Identification[J]. 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

DENG Julong.The control problem of grey systems[J]. Systems and Control Letters, 1982,11(5):285-294.

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.

MA Xin, HOU Zhijian, JIANG Chuanwen.Grey forecasting electricity forward price [J].Journal of Grey System, 2003, 15(3):263-266.

程晓鑫,周渝慧. 基于灰色改进模型的电价预测[J]. 华北电力大学学报,2006, 33(1):47-50.

CHENG Xiaoxin, ZHOU Yuhui. Research on electricity price prediction based on improved grey model [J]. Journal of North China Electric Power University, 2006, 33(1):47-50.

吴兴华,周晖.基于残差周期修正的灰色电价预测模型[J].电网技术, 2008, 32(8):67-70.

WU Xinghua, ZHOU Hui. A grey model of electricity price forecasting based on period residual modification [J]. Power System Technology, 2008, 32(8):67-70.

党耀国,刘思峰,刘斌.以x(1)(n)为初始条件的GM模型[J].中国管理科学, 2005, 13(1):132-135.

DANG Yaoguo, LIU Sifeng, LIU Bin.The GM models that x(1)(n)be taken as initial value[J].Chinese Journal of Management Science, 2005, 13(1):132-135.

谭冠军.GM(1,1)模型的背景值构造方法和应用(Ⅰ)[J].系统工程理论与实践, 2000, 3(4):98-103.

TAN Guanjun.The structure method and application of background value in grey system GM(1,1)model(Ⅰ)[J].Systems Engineering-Theory Practice, 2000, 3(4):98-103.

董奋义,田军.背景值和初始条件同时优化的GM(1,1)模型[J].系统工程与电子技术, 2007, 29(3):464-466.

DONG Fenyi, TIAN Jun.Optimization integrated background value with original condition for GM(1,1)[J].Systems Engineering and Electronics, 2007,29(3):464-466.

KENNEDY J, EBERHART R C. Particle swarm optimization [C]∥Proc IEEE Int Conf Neural Networks. Piscataway, NJ,USA: IEEE Press, 1995:1942-1948.

陈贵敏,贾建援,韩琪.粒子群优化算法的惯性权值递减策略研究[J].西安交通大学学报,2006,40(1):53-56.

CHEN Guimin, JIA Jianyuan, HAN Qi. Study on the strategy of decreasing inertial weight in particle swarm optimization algorithm [J].Journal of Xi'an Jiaotong University,2006,40(1):53-56.

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