本书旨在充分利用所有可获取的数据，并将其转化为实际信息，并将其纳入电力用户行为建模和配用电系统运行中。本书首先概述了智能电表数据分析的最新发展。由于数据管理是进一步智能电表数据分析及其应用的基础，因此随后研究了数据管理的三个问题，即数据压缩、异常检测和数据生成。接下面的工作试图模拟复杂的电力用户行为。具体工作包括负荷分析、模式识别、个性化价格设计、社会人口信息识别和家庭行为编码。在此基础上，本书在时空尺度上扩展了消费者行为。介绍了用户聚合、单个负载预测和聚合负载预测等工作。我们希望这本书能够启发读者去定义新的问题，应用新的方法，并通过大量的智能电表数据，甚至电力系统中的其他监测数据，获得一些有意思的结论。

1 Overview of Smart Meter Data Analytics
1.1 Introduction
1.2 Load Analysis
1.2.1 Bad Data Detection
1.2.2 Energy Theft Detection
1.2.3 Load Profiling
1.2.4 Remarks
1.3 Load Forecasting
1.3.1 Forecasting Without Smart Meter Data
1.3.2 Forecasting with Smart Meter Data
1.3.3 Probabilistic Forecasting
1.3.4 Remarks
1.4 Load Management
1.4.1 Consumer Characterization
1.4.2 Demand Response Program Marketing
1.4.3 Demand Response Implementation
1.4.4 Remarks
1.5 Miscellanies
1.5.1 Connection Verification
1.5.2 Outage Management
1.5.3 Data Compression
1.5.4 Data Privacy
1.6 Conclusions
References
2 Electricity Consumer Behavior Model
2.1 Introduction
2.2 Basic Concept of ECBM
2.2.1 Definition
2.2.2 Connotation
2.2.3 Denotation
2.2.4 Relationship with Other Models
2.3 Basic Characteristics of Electricity Consumer Behavior
2.4 Mathematical Expression of ECBM
2.5 Research Paradigm of ECBM
2.6 Research Framework of ECBM
2.7 Conclusions
References
3 Smart Meter Data Compression
3.1 Introduction
3.2 Household Load Profile Characteristics
3.2.1 Small Consecutive Value Difference
3.2.2 Generalized Extreme Value Distribution
3.2.3 Effects on Load Data Compression
3.3 Feature-Based Load Data Compression
3.3.1 Distribution Fit
3.3.2 Load State Identification
3.3.3 Base State Discretization
3.3.4 Event Detection
3.3.5 Event Clustering
3.3.6 Load Data Compression and Reconstruction
3.4 Data Compression Performance Evaluation
3.4.1 Related Data Formats
3.4.2 Evaluation Index
3.4.3 Dataset
3.4.4 Compression Efficiency Evaluation Results
3.4.5 Reconstruction Precision Evaluation Results
3.4.6 Performance Map
3.5 Conclusions
References
4 Electricity Theft Detection
4.1 Introduction
4.2 Problem Statement
4.2.1 Observer Meters
4.2.2 False Data Injection
4.2.3 A State-Based Method of Correlation
4.3 Methodology and Detection Framework
4.3.1 Maximum Information Coefficient
4.3.2 CFSFDP-Based Unsupervised Detection
4.3.3 Combined Detecting Framework
4.4 Numerical Experiments
4.4.1 Dataset
4.4.2 Comparisons and Evaluation Criteria
4.4.3 Numerical Results
4.4.4 Sensitivity Analysis
4.5 Conclusions
References
5 Residential Load Data Generation
5.1 Introduction
5.2 Model
5.2.1 Basic Framework
5.2.2 General Network Architecture
5.2.3 Unclassified Generative Models
5.2.4 Classified Generative Models
5.3 Methodology
5.3.1 Data Preprocessing
5.3.2 Model Training
5.3.3 Metrics
5.4 Case Studies
5.4.1 Data Description
5.4.2 Unclassified Generation
5.4.3 Classified Generation
5.5 Conclusion
References
6 Partial Usage Pattern Extraction
6.1 Introduction
6.2 Non-negative K-SVD-Based Sparse Coding
6.2.1 The Idea of Sparse Representation
6.2.2 The Non-negative K-SVD Algorithm
6.3 Load Profile Classification
6.3.1 The Linear SVM
6.3.2 Parameter Selection
6.4 Evaluation Criteria and Comparisons
6.4.1 Data Compression-Based Criteria
6.4.2 Classification-Based Criteria
6.4.3 Comparisons
6.5 Numerical Experiments
6.5.1 Description of the Dataset
6.5.2 Experimental Results
6.5.3 Comparative Analysis
6.6 Further Multi-dimensional Analysis
6.6.1 Characteristics of Residential & SME Users
6.6.2 Seasonal and Weekly Behaviors Analysis
6.6.3 Working Day and Off Day Patterns Analysis
6.6.4 Entropy Analysis
6.6.5 Distribution Analysis
6.7 Conclusions
References
7 Personalized Retail Price Design
7.1 Introduction
7.2 Problem Formulation
7.2.1 Problem Statement
7.2.2 Consumer Problem