音像非侵入式负荷识别:理论、技术与应用刘辉

1 Introduction 1

1.1 Overview of the Non-intrusive Load Monitoring 1

1.1.1 The Non-intrusive Load Monitoring 1

1.1.2 Overview of Recent Research in Non-intrusive Load Monitoring 3

1.2 Fundamental Key Problems of Non-intrusive Load Monitoring 8

1.2.1 Event Detection in Non-intrusive Load Monitoring 8

1.2.2 Feature Extraction in Non-intrusive Load Monitoring 10

1.. Load Identification in Non-intrusive Load Monitoring 13

1.2.4 Energy Forecasting in Smart Buildings 15

1.3 Scope of This Book 17

References 19

2 Detection of Transient Events in Time Series

2.1 Introduction

2.2 Cumulative Sum Based Transient Event Detection Algorithm 24

2.2.1 Mathematical Description of Change Point Detection 24

2.2.2 Parametric CUSUM Algorithm 24

2.. Non-parametric CUSUM Algorithm 25

2.2.4 Sliding Windows Based on Two-Sided CUSUM Algorithm 26

2.2.5 Original Dataset 26

2.2.6 Evaluation Criteria and Results Analysis 29

. Generalized Likelihood Ratio 36

..1 The Theoretical Basis of GLR 36

..2 Comparison of Event Detection Results 37

2.4 Sequential Probability Ratio Test 39

2.4.1 The Theoretical Basis of SPRT 39

2.4.2 Comparison of Event Detection Results 40

2.5 Experiment Analysis 42

2.5.1 The Results of Three Kinds o Aorithms. 42

2.5.2 Conclusion 42

References 43

3 Appliance Signature Extraction 45

3.1 Introduction 45

3.1.1 Background 45

3.1.2 Feature Evaluation Indices 46

3.1.3 Classification Evaluation Indices 48

3.1.4 Data Selection 49

3.2 Features Based on Conventional Physical Definition 50

3.2.1 The Theoretical Basis of Physical Definition Features 50

3.2.2 Feature Extraction 52

3.. Feature Evaluation 53

3.2.4 Classification Results 54

3.3 Features Based on Time-Frequency Analysis 55

3.3.1 The Theoretical Basis of Harmonic Features 55

3.3.2 Feature Extraction 56

3.3.3 Feature Evaluation 58

3.3.4 Classification Results 59

3.4 Features Based on VI Image 62

3.4.1 The Theoretical Basis of VI Image Features 62

3.4.2 Feature Extraction 65

3.4.3 Feature Evaluation 68

3.4.4 Classification Results 70

3.5 Features Based on Adaptive Methods 73

3.5.1 The Theoretical Basis of Adaptive Features 73

3.5.2 Feature Extraction 74

3.5.3 Classification Results 74

3.6 Experimental Analysis 76

3.6.1 Comparative Analysis of Classification Performance 76

3.6.2 Conclusion 76

References 77

4 Appliance Identification Based on Template Matching 79

4.1 Introduction 79

4.1.1 Background 79

4.1.2 Data Preprocessing of the PLA Dataset 80

4.2 Appliance Identification Based on Decision Tree 82

4.2.1 The Theoretical Basis of Decision Tree 82

4.2.2 Steps of Modeling 83

4.. Classification Results 84

4.3 Appliance Identification Based on KNN Algorithm 85

4.3.1 The Theoretical Basis of KNN 85

4.3.2 Steps of Modeling 86

4.3.3 Classification Results 87

4.4 Appliance Identification Based on DTW Algorithm 88

4.4.1 The Theoretical Basis of DTW 88

4.4.2 Steps of Modeling 90

4.4.3 Classification Results 91

4.5 Experiment Analysis 91

4.5.1 Model Framework 91

4.5.2 Comparative Analysis of Classification Performance 92

4.5.3 Conclusion 100

References 102

5 Steady-State Current Decoition Based Appliance Identification 105

5.1 Introduction 105

5.2 Classical Steady-State Current Decoition Models 107

5.2.1 Model Framework 107

5.2.2 Classical Features of Steady-State Decoition and the Feature Extraction Methd 08

5.. Classical Methods in Steady-State Current Decoition 115

5.2.4 Performance of the Various Features and Models 116

5.3 Current Decoition Models Based on Harmonic Phasr 20

5.3.1 Model Framework 120

5.3.2 Novel Features of Steady-State Current Decoition 121

5.3.3 Multi-objective Optimization Methods in Steady-State Current Decoition 124

5.3.4 Performance of the Novel Features an Mt-objective Optimization Models 126

5.4 Current Decoition Models Based on Non-negative Matrix Factr 29

5.4.1 Model Framework 129

5.4.2 Reconstruction of the Data 129

5.4.3 Non-negative Matrix Factorization Method of the Current Decoition 131

5.4.4 Evaluation of the NMF Method in Current Decoition 133

5.5 Experiment Analysis 135

5.5.1 Data Generation 135

5.5.2 Comparison Analysis of the Features Used in the Steady-State Decoition 136

5.5.3 Comparison Analysis of the Models Used in the Steady-State Decoition 137

5.5.4 Conclusion 138

References 141

6 Machine Learning Based Appliance Identification 141

6.1 Introduction 142

6.2 Appliance Identification Based on Extreme Learning Machine 142

6.2.1 The Theoretical Basis of ELM 143

6.2.2 Steps of Modeling 143

6.. Classification Results 145

6.3 Appliance Identification Based on Support Vector Machine 145

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