摘要
为了提高电力知识文本挖掘的性能,采用FP-Growth算法对影响电力需求的强关联因素进行挖掘,运用广义回归神经网络(General Regression Neural Network,GRNN)算法实现电力需求预测。首先,对待挖掘的电力文本进行指标提取并编码,生成电力文本初始FP-Tree;接着采用FP-Growth算法遍历所有FP-Tree,生成频繁集,过滤掉小于最小支持度的项,留下频数较高的频繁项;然后根据更新后的FP-Tree统计关联项,选择与总用电量增长率关联强的变量生成训练样本;最后采用GRNN算法对电力需求文本进行训练,输入电力需求预测样本,设置平滑因子,通过模式层的输出和加权求和来获得电力需求预测结果。实验结果证明,通过合理设置最小支持度和GRNN的平滑因子,能够获得较好的电力文本挖掘性能,与常用挖掘算法相比,所提算法能够获得更高的电力需求预测准确率。
In order to improve the performance of power knowledge text mining,FP-Growth algorithm is used to mine the strong correlation factors that affect the power demand,and GRNN algorithm is used to realize the power demand forecasting.Firstly,the index of the power text to be mined is extracted and encoded to generate the initial FP-Tree.Then,FP-Growth algorithm traverses all FP-Tree generated frequent sets,filters out the items less than the minimum support,leaves the frequent items with higher frequency.And then according to the updated FP-Tree statistical correlation items,it selects variables with strong correlation with the growth rate of total electricity consumption to generate training samples.Finally,the GRNN algorithm is used to train the power demand text,input the power demand forecasting samples,set the smoothing factor,and obtain the power demand forecasting results through the output and weighted sum of the mode layer.Experimental results show that better power text mining performance can be obtained by setting the minimum support and the smoothing factor of GRNN.Compared with common mining algorithms,this algorithm can obtain higher accuracy of power demand forecasting.
作者
白勇
张占龙
熊隽迪
BAI Yong;ZHANG Zhan-long;XIONG Jun-di(Chongqing Electric Power Specialist University,Chongqing 400053,China;Chongqing University,Chongqing 400030,China)
出处
《计算机科学》
CSCD
北大核心
2021年第8期86-90,共5页
Computer Science
基金
国家自然科学基金(52007011)
重庆市教委科学技术重点研究项目(KJZD-K202002601)。
