非侵入式负荷识别:理论、技术与应用
全新正版 假一赔十 可开发票
￥ 119.11 7.1折 ￥ 168 全新
库存2件
作者刘辉
出版社科学出版社
ISBN9787030629265
出版时间2020-03
装帧精装
开本其他
定价168元
货号1202010488
上书时间2024-08-24
商品详情
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目录
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
1.2.3 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 23
2.1 Introduction 23
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
2.2.3 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
2.3 Generalized Likelihood Ratio 36
2.3.1 The Theoretical Basis of GLR 36
2.3.2 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 of Algorithms. 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
3.2.3 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 PLAID 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
4.2.3 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 Decomposition Based Appliance Identification 105
5.1 Introduction 105
5.2 Classical Steady-State Current Decomposition Models 107
5.2.1 Model Framework 107
5.2.2 Classical Features of Steady-State Decomposition and the Feature Extraction Method 108
5.2.3 Classical Methods in Steady-State Current Decomposition 115
5.2.4 Performance of the Various Features and Models 116
5.3 Current Decomposition Models Based on Harmonic Phasor 120
5.3.1 Model Framework 120
5.3.2 Novel Features of Steady-State Current Decomposition 121
5.3.3 Multi-objective Optimization Methods in Steady-State Current Decomposition 124
5.3.4 Performance of the Novel Features and Multi-objective Optimization Models 126
5.4 Current Decomposition Models Based on Non-negative Matrix Factor 129
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 Decomposition 131
5.4.4 Evaluation of the NMF Method in Current Decomposition 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 Decomposition 136
5.5.3 Comparison Analysis of the Models Used in the Steady-State Decomposition 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
6.2.3 Classification Results 145
6.3 Appliance Identification Based on Support Vector Machine 145
……
内容摘要
非侵入式负荷识别技术作为智能电网需求侧能源管理的基础，在优化电网供求关系、促进节能减排等方面具有广阔的应用前景。本书全面介绍了非侵入式负荷识别的相关的基本理论、关键技术和应用实例。全书分为10章，第1章介绍非侵入式负荷识别的技术背景、相关定义和涉及的关键基础问题；接着分4大部分展开，第一篇介绍非侵入式负荷分解的前处理过程，包括第2章介绍的负荷时序状态变动检测和第3章介绍的用电负荷差异化特征提取；第二篇介绍非侵入式负荷统计识别方法，重点讨论基于模板匹配的负荷识别模型和基于稳态电流分解的负荷识别模型；第三篇介绍用电负荷智能识别方法，包括基于机器学习的负荷识别模型，基于隐含马尔可夫的负荷识别模型和基于深度学习的负荷识别模型；第四篇介绍非侵入式负荷识别在智能用电负荷预测方法中的应用，包括用电负荷时序确定性预测和用电负荷时序区间预测。各部分内容都附有实例分析，帮助读者深入理解相关内容、激发创新灵感。
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