[1]陈昊,万秋兰,王玉荣.基于自回归条件密度模型的短期负荷预测方法[J].东南大学学报(自然科学版),2014,44(3):561-566.[doi:10.3969/j.issn.1001-0505.2014.03.020]
 Chen Hao,Wan Qiulan,Wang Yurong.Short term load forecasting method based on auto-regressive conditional density model[J].Journal of Southeast University (Natural Science Edition),2014,44(3):561-566.[doi:10.3969/j.issn.1001-0505.2014.03.020]
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基于自回归条件密度模型的短期负荷预测方法()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
44
期数:
2014年第3期
页码:
561-566
栏目:
电气工程
出版日期:
2014-05-16

文章信息/Info

Title:
Short term load forecasting method based on auto-regressive conditional density model
作者:
陈昊万秋兰王玉荣
东南大学电气工程学院, 南京 210096
Author(s):
Chen Hao Wan Qiulan Wang Yurong
School of Electrical Engineering, Southeast University, Nanjing 210096, China
关键词:
自回归条件密度模型 时变参数 高阶矩 极大似然估计 短期负荷预测
Keywords:
auto-regressive conditional density(ARCD)model time-varying parameter high order moment maximum likelihood estimate short term load forecasting
分类号:
TM714
DOI:
10.3969/j.issn.1001-0505.2014.03.020
摘要:
基于对负荷时间序列高阶矩时变特征的研究,提出了一种基于自回归条件密度模型的短期负荷预测新方法.该方法通过引入含时变参数的有偏分布,对负荷时间序列二阶以上矩信息进行了分析和描述.基于南京地区日用电量实际历史数据,分析了该负荷时间序列的时变高阶矩特征,建立了自回归条件密度模型.使用条件对数极大似然估计对模型参数进行了估计,实现了短期负荷预测,验证了该方法的可行性和有效性.结合算例中自回归条件密度模型时变参数的取值范围,推导了时变参数与条件高阶矩的数理关系,给出了一种刻画时间序列时变高偏度(三阶矩)、时变高峰度(四阶矩)的途径.算例分析表明,基于有偏t分布的自回归条件密度负荷预测模型的预测效果良好.
Abstract:
By analyzing the time varying characteristics of the high order moments of load time series, a novel short term load forecasting method is proposed based on the auto-regressive conditional density(ARCD)model. By introducing the skewed distribution with time-varying parameter, the proposed method can analyze and describe the implicated information in moments higher than the second order of load time series. Based on the historical daily practical power consumption data of Nanjing, the time-varying high order moments of the load time series are examined, and the proposed ARCD load forecasting model is established. By using the conditional maximum likelihood estimation(CMLE), the parameters are estimated, and short term load forecasting is provided. As a result, the feasibility and effectiveness of the proposed model is validated. The mathematical relationship between time-varying parameter and conditional high moments is deduced considering the range of the time-varying parameter in the ARCD model. Specifically, the methods for describing the third order moment and the fourth order moment of time series are illustrated. Numerical results indicate that the ARCD model with skewed t distribution provides satisfying forecasting results.

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备注/Memo

备注/Memo:
收稿日期: 2013-10-22.
作者简介: 陈昊(1980—),男,博士生;万秋兰(联系人),女,博士,教授,博士生导师,qlwan@seu.edu.cn.
引用本文: 陈昊,万秋兰,王玉荣.基于自回归条件密度模型的短期负荷预测方法[J].东南大学学报:自然科学版,2014,44(3):561-566. [doi:10.3969/j.issn.1001-0505.2014.03.020]
更新日期/Last Update: 2014-05-20