|本期目录/Table of Contents|

[1]彭鹏,彭佳红.基于多元线性回归模型的电力负荷预测研究[J].中国安全生产科学技术,2011,7(9):158-161.
 PENG Peng,PENG Jia-hong.Research on the prediction of power load based on Multiple linear regression model[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2011,7(9):158-161.
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基于多元线性回归模型的电力负荷预测研究()
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
7
期数:
2011年9期
页码:
158-161
栏目:
职业安全卫生管理与技术
出版日期:
2011-09-30

文章信息/Info

Title:
Research on the prediction of power load based on Multiple linear regression model
文章编号:
1673-193X(2011)-09-0158-04
作者:
彭鹏1彭佳红2
1.华北电力大学经济与管理学院,北京 102206
2. 湖南农业大学 信息科学技术学院,长沙? 410128
Author(s):
PENG Peng1 PENG Jia-hong2
1.School of Economics and Management, North of China Electric Power University, Beijing 102206, China
2.College of Information Science and Technology, Hunan Agricultural University, Changsha 410128, China
关键词:
全社会用电量国内生产总值电力系统负荷预测回归模型
Keywords:
Electricity Consumption Gross domestic product power systems load forecasting Regression Model
分类号:
TM714
DOI:
-
文献标志码:
A
摘要:
电力负荷预测是电力系统规划和电网运行的重要内容、前提和基础。科学、准确的电力需求预测对电力工业的健康发展,乃至对整个国民经济的发展均有着十分重要的意义。针对我国1995年至2008年人口、GDP和全社会用电量的历史数据,基于多元线性回归分析进行中、长期电力负荷预测,多元线性回归模型通过变量GDP和人口进行全社会用电量的电力负荷定量预测。结果表明模型的有效性,为电力负荷预测的滚动修正,进而为电力负荷控制和预测提供科学依据。
Abstract:
The prediction of power load is important content,prerequisite and basis of the planning of power system and running of power grid.Scientific and accurate prediction is of great importance to the healthy development of power industry and even the entire national economy.This paper gives a prediction for interim and long term power load on the basis of multi factor line regression analysis,the model in this paper uses the variant GDP and population to predict the total social electricity consumption quantitatively,history datums on the population,GDP and total social electricity consumption are utlized in this model, the span of datums is from 1995 to 2008.The result shows that the model is effective to the rolling rectifying of power load prediction and further can provide foundation for the controlling and prediction of power load.

参考文献/References:

[1]蒋梁瑜,门可佩.中国电力与GDP 协调发展预测分析[J], 统计教育,2009,10:3-7Jiang Liangyu, Men Kepei. Forecast and Analysis of Coordinated Development between the Electric Power and GDP in China[J], Statistical Thinktank, 2009,10:3-7[2] 谢洁树,电力负荷预测的方法研究[J], 灯与照明,2008,3:52-55Xie Jieshu. The Research of Electric Power Load Forcasting Methods[J],Light & Lighting, 2008,3:52-55[3] 胡杰,文闪闪,胡导福等.电力负荷预测常用方法的分析比较与应用[J],湖北电力,2008(32),2:13-15HU Jie ,WEN Shanshan,HU Daofu,et al..Analyzing ,Comparing And Applying the Common Methods of Electric Power Load Forcecasting[J], Hubei Electric Power, 2008(32),2:13-15[4] 王东,回归算法在电力负荷预测中的应用[J], 仪器仪表用户,2009(13),6:42-43WANG Dong.Several Regression Algorithms applied in short-term Load forecasting[J],Electronic Instrumentation Customer, 2006(26),3:51-53[5] 游仕洪,程浩忠,谢宏.应用模糊线性回归模型预测中长期电力负荷[J], 电力自动化设备,2006(26),3:51-53YOU Shi hong,CHENG Haozhong,,XIE Hong. Mid - and Long- term Load Forecast Based on Fuzzy Linear Regression Model[J], Electric Power Automation Equipment, 2006(26),3:51-53[6] 徐玉华. 中长期电力负荷预测方法分析[J], 宁夏电力,2007,4:6-7Xu Yuhua. The middle and long term predictive methods for electric power load[J], Ningxia Electric Power, 2007,4:6-7[7]中华人民共和国国家统计局,中国统计年鉴2009[M].北京:中国统计出版社,2009.9[8] 朱东晓,曹树华,赵磊等.电力负荷预测技术及其应用[M].北京:中国电力出版社,2009:187-233[9] 肖国泉,王春张,福伟.电力负荷预测[M].北京:中国电力出版社,2001:147-197[10] 卢纹岱. SPSS for Windows 统计分析(第3版)[M]. 北京:电子工业出版社,2007:294-365[11] 张文彤,董伟. SPSS统计分析高级教程[M]. 北京:高等教育出版社,2006:91-117[12] 中华人民共和国国家统计局,中国统计年鉴2010[M].北京:中国统计出版社,2010.9

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备注/Memo

备注/Memo:
收稿日期:2011-03-10
通讯作者:彭佳红,教授。
基金项目:国家高等学校博士学科点专项科研基金(编号:20094320110001);湖南省教育厅科学研究项目(编号:10C0789)
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