电力系统自动化2016，Vol.40Issue(21)：128-136,9.DOI:10.7500/AEPS20160201004
基于因子隐马尔可夫模型的负荷分解方法及灵敏度分析
Load Disaggregation Method Based on Factorial Hidden Markov Model and Its Sensitivity Analysis
摘要
Abstract
As the key technology in smart grid,load disaggregation is important for the tasks such as load forecasting,demand side management and power security.The accuracy of the traditional methods subj ects to the dimension of load signatures,the sampling frequency and the stability of load profile.In this paper,a Factorial Hidden Markov Model (FHMM) based load disaggregation method is proposed,which contains the load state disaggregation through extended Viterbi algorithm and the load allocation based on integer programming.The load is modeled as FHMM,and we extend the Viterbi algorithm to solve the FHMM directly.The proposed method is insensitive to the stability and accuracy of power data,so it is suitable for the residential and industrial devices.Meanwhile,through the sensitivity analysis of Viterbi algorithm,the relationship between the optimal states and the disturbance of the observation is established,which is significant for the reliability evaluation of the optimal states.关键词
隐马尔可夫模型/因子隐马尔可夫模型/负荷分解/灵敏度分析Key words
hidden Markov model/factorial hidden Markov model/load disaggregation/sensitivity analysis引用本文复制引用
陈思运,高峰,刘烃,翟桥柱,管晓宏..基于因子隐马尔可夫模型的负荷分解方法及灵敏度分析[J].电力系统自动化,2016,40(21):128-136,9.基金项目
国家自然科学基金资助项目(61473218) （61473218）
国家重点研发计划资助项目(2016YFB0901904)。@@@@This work is supported by National Natural Science Foundation of China(No.61473218)and National Key Research and Development Program of China(No.2016YFB0901904) （2016YFB0901904）
