基于KPCA 和 STFT 非侵入式负荷监控（Matlab代码实现）

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📋📋📋本文目录如下：🎁🎁🎁

目录

💥1 概述

📚2 运行结果

🎉3 参考文献

🌈4 Matlab代码实现

💥1 概述

针对目前非侵入式负荷监测仅能识别单个家用电器、多种家用电器同时运行识别率低的问题，本文提出了一种提取电流波形特征以识别家用电器的方法。使用短时傅里叶变换（STFT）和核PCA技术来提取这些特征。定义特征后，分类器k-最近邻（kNN），支持向量机（SVM），线性判别分析（LDA），随机森林（RF）和极限学习机（ELM）用于设备（或设备组合）识别。

📚2 运行结果

部分代码：

%% Classification
%% KNN
% Train
start_time_train=cputime;
KNN = fitcknn(Xtrainp,Ytrain,'NumNeighbors',5);
end_time_train=cputime;
TrainTime_knn=end_time_train-start_time_train

% Test
start_time_test=cputime;
label_knn = predict(KNN,Xtestp);
end_time_test=cputime;
TestTime_knn=end_time_test-start_time_test
AccRate_Knn = sum(Ytest' == label_knn)/length(Ytest)

%% SVM
% Train
start_time_train=cputime;
tSVM = templateSVM('Standardize',1,'KernelFunction','linear');
SVM = fitcecoc(Xtrainp,Ytrain,'Learners',tSVM);
end_time_train=cputime;
TrainTime_svm=end_time_train-start_time_train

% Test
start_time_test=cputime;
label_svm = predict(SVM,Xtestp);
end_time_test=cputime;
TestTime_svm=end_time_test-start_time_test
AccRat_svm = sum(Ytest' == label_svm)/length(Ytest)

%% LDA
% Train
start_time_train=cputime;
LDA = fitcdiscr(Xtrainp,Ytrain);
end_time_train=cputime;
TrainTime_lda=end_time_train-start_time_train

% Test
start_time_test=cputime;
label_lda = predict(LDA,Xtestp);
end_time_test=cputime;
TestTime_lda=end_time_test-start_time_test
AccRate_lda = sum(Ytest' == label_lda)/length(Ytest)

%% Decision tree
% train
start_time_train=cputime;
TREE = TreeBagger(150,Xtrainp,Ytrain,'OOBPredictorImportance','on');
end_time_train=cputime;
TrainTime_tree=end_time_train-start_time_train

% Test
start_time_test=cputime;
label_tree = str2double(predict(TREE,Xtestp));
end_time_test=cputime;
TestTime_tree=end_time_test-start_time_test
AccRate_tree = sum(Ytest' == label_tree)/length(Ytest)

%% ELM
[TrainingTime, TestingTime, ~, AccRate_elm, label_elm] = elm_aece(Xtrainp',...
Ytrain, Xtestp', Ytest, 1, 1500, 'sig');
AccRate_elm

%% Statistics
C_svm = confusionmat(Ytest',label_svm);
C_lda = confusionmat(Ytest',label_lda);
C_knn = confusionmat(Ytest',label_knn);
C_tree = confusionmat(Ytest',label_tree);
C_elm = confusionmat(Ytest',label_elm);
[result_svm,reference_svm]= getStatistical(C_svm);
[result_knn,reference_knn]= getStatistical(C_knn);
[result_lda,reference_lda]= getStatistical(C_lda);
[result_tree,reference_tree]= getStatistical(C_tree);
[result_elm,reference_elm]= getStatistical(C_elm);