电测与仪表2017，Vol.54Issue(17)：11-17,7.
基于遗传优化的非侵入式居民负荷辨识算法
A non-intrusive residential load identification algorithm based on genetic optimization
摘要
Abstract
Load online monitoring can provide real-time power consumption information for the grid and users , which is an effective method to support energy management and load forecasting work .Traditional method within intrusive mode is difficult to promote a wide range of applications , so non-intrusive load monitoring method ( NILM) has impor-tant significance .Load identification is very important to NILM .Considering the residential load typical characteristic analysis , a non-intrusive residential load identification algorithm based on genetic optimization is proposed .The algo-rithm based on load characteristic , including active power and current effective value , uses three different encoding methods to structure fitness function , and ultimately determines the specific type of load by genetic optimization , and then, the effectiveness of the algorithm is verified by the actual sampling load data .Experimental results show that the algorithm can achieve residential load identification , and improves the speed of constriction and accuracy of the identi-fication.关键词
非侵入式/负荷监测/居民负荷/负荷辨识/遗传算法Key words
non-intrusive/load monitoring/residential load/load identification/genetic optimization(GA)分类
信息技术与安全科学引用本文复制引用
祁兵,韩璐..基于遗传优化的非侵入式居民负荷辨识算法[J].电测与仪表,2017,54(17):11-17,7.基金项目
国家重点研发计划资助项目课题(2016YFB0901104) （2016YFB0901104）
中央高校基本科研业务费专项资金资助项目(2016MS13) （2016MS13）
