摘要
目前负荷中长期预测时选取相关的社会经济指标没有统一的理论依据。文中将数据挖掘技术应用到电量增长的关联性分析中,从全国30个省(自治区、直辖市)的历史数据中选取25项相关指标,采用3种不同的隶属度函数进行赋值。在此基础上利用Apriori算法计算不同指标与用电量增长相关的模糊置信度,辨识出国内生产总值(GDP)、工业总产值、进出口总额、固定资产投资、居民人均可支配收入等与用电量增长较为相关的主导因素,并结合自组织映射神经网络获取中国用电量增长的一般性规律。该研究思路为年度负荷预测相关因素的选取提供新的策略。
The selection of relevant socio-economic indicators for power load mid-long term forecasting lacks theoretical foundation in recent research papers.Data mining techniques are introduced into association analysis of power consumption growth.Twenty-five relevant indicators are selected from statistics of China＇s thirty provinces,autonomous regions and municipalities.Three membership functions are adopted to assign values and Apriori algorithm is then used to calculate fuzzy confidence value between power consumption and different influencing factors.Several dominant factors including gross domestic product（GDP）,industrial output value,total value of imports and exports,total investment in fixed assets and per capita disposable income,are identified in power load growth.And general growth rule of China＇s power consumption is finally obtained based on self-organizing map neural network.The research procedure provides a new strategy to choose correlative factors in yearly prediction of power load.
出处
《电力系统自动化》
EI
CSCD
北大核心
2010年第23期30-35,共6页
Automation of Electric Power Systems
关键词
用电量
负荷预测
关联分析
数据挖掘
模糊关联规则
自组织映射神经网络
power consumption
load forecasting
association analysis
data mining
fuzzy association rule
self-organizing map neuralnetwork
