计算机科学 ›› 2021, Vol. 48 ›› Issue (8): 86-90.doi: 10.11896/jsjkx.210600031
白勇1, 张占龙2, 熊隽迪1
BAI Yong1, ZHANG Zhan-long2, XIONG Jun-di1
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。
中图分类号:
[1]BAI K F,YANG B,WEI J.Review of power text mining techno-logy[J].Electronic Technology and Software Engineering,2019,168(22):143-144. [2]ZHANG P M,GU J,ZHANG Q.Model Prediction for Grid-connected Inverter Control Strategy under Weak Power Grid[J].Journal of Chongqing Technology and Business University(Na-tural Science Edition),2019,36(6):100-105. [3]GUO B J,BIAN X X.Application of Improved Genetic Algo-rithms to Distribution Network Fault Location with DG[J].Journal of Chongqing Technology and Business University(Na-tural Science Edition),2019,36(5):24-30. [4]KONG C X,YANG A,ZHU L J.Traveling Wave Fault Location of Double-ended Transmission Lines Based on Wavelet Transform[J].Journal of Chongqing Technology and Business University(Natural Science Edition),2019,36(5):31-36. [5]LIU Y H,HUANG S W,MEI S W,et al.Analysis method of cascading failure mode in power system based on sequential pattern mining[J].Power System Automation,2019,43(6):34-40. [6]WANG C Y,JIANG Q Y,TANG Y J,et al.Power dispatching fault diagnosis based on text mining of alarm signal [J].Power Automation Equipment,2019,39(4):126-132. [7]GLIGOR A,VLASA I,DUMITRU C D,et al.Power DemandForecast for Optimization of the Distribution Costs[J].Procedia Manufacturing,2020,46:384-390. [8]JR A,MKK B.Map-optimize-reduce:CAN tree assisted FP-growth algorithm for clusters based FP mining on Hadoop[J].Future Generation Computer Systems,2020,103:111-122. [9]KALITA J,BALAS V E,BORAH S,et al.FP-Growth:A Step to Remove the Bottleneck of FP-Tree[J].Advances in Intelligent Systems and Computing,2019,176:285-296. [10]ZHU X,LIU Y.An efficient frequent pattern mining algorithm using a highly compressed prefix tree[J].Intelligent Data Ana-lysis,2019,23:153-173. [11]GAO Q,WAN X D.Parallel FP growth algorithm based on load balancing [J].Computer Engineering,2019,498(3):38-41,46. [12]PEI X.Application of GRNN Neural Network in Grain Yield Prediction[J].Hans Journal of Data Mining,2020,10(4):247-253. [13]BANI-HANI D,KHASAWNEH M.A Recursive General Re-gression Neural Network(R-GRNN) Oracle for Classification Problems[J].Expert Systems with Applications,2019,135:273-286. [14]WANG G,ZHANG C,LIU Y,et al.A global and updatableECG beat classification system based on recurrent neural networks and active learning[J].Information Science,2018,12(3)221-228. [15]IZONIN I,KRYVINSKA N,TKACHENKO R,et al.An Ex-tended-Input GRNN and its Application[J].Procedia Computer Science,2019,160:578-583. [16]SONG C,WANG L,HOU J,et al.The optimized GRNN based on the FDS-FOA under the hesitant fuzzy environment and its application in air quality index prediction[J].Applied Intelligence,2021(3):1-12. [17]FANNAS L Y,SASI A B.Off-Line Signature Recognition Based on Angle Features and GRNN Neural Networks[J].International Journal of Parallel Programming,2019,7:229-233. [18]YANG S X,CAO Y,LIU D,et al.RS-SVM forecasting model and power supply and demand forecasting [J].Journal of Central South University,2011,18(6):2074-2079. [19]CARNEIRO H,PEDREIRA C E,FRAN F,et al.A universal multilingual weightless neural network tagger via quantitative linguistics[J].Neural Networks,2017(11):85-101. |
[1] | 马力文, 周颖. 改善STARTUP阶段空窗现象的BBR单边适应算法 BBR Unilateral Adaptation Algorithm for Improving Empty Window Phenomenon in STARTUP Phase 计算机科学, 2022, 49(2): 321-328. https://doi.org/10.11896/jsjkx.201200266 |
[2] | 朱岸青, 李帅, 唐晓东. Spark平台中的并行化FP_growth关联规则挖掘方法 Parallel FP_growth Association Rules Mining Method on Spark Platform 计算机科学, 2020, 47(12): 139-143. https://doi.org/10.11896/jsjkx.191000110 |
[3] | 叶俊,张正军. 基于DS-Adaboost算法的人脸检测 Face Detection Based on DS-Adaboost Algorithm 计算机科学, 2013, 40(Z11): 318-319. |
[4] | 余嘉元 汪存友. 基于神经网络的项目参数估计方法 计算机科学, 2008, 35(3): 134-136. |
[5] | 秦亮曦 李谦 史忠植. 基于排序FP-树的频繁模式高效挖掘算法 计算机科学, 2005, 32(4): 31-33. |
[6] | 袁鼎荣 张师超. 基于频繁链表的频繁集的挖掘算法 计算机科学, 2003, 30(7): 165-166. |
|