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 7.2.3 Compatible Incentive Design 7.2.4 Retailer Problem 7.2.5 Data-Driven Clustering and Preference Discovering 7.2.6 Integrated Model 7.3 Solution Methods 7.3.1 Framework 7.3.2 Piece-Wise Linear Approximation 7.3.3 Eliminating Binary Variable Product 7.3.4 CVaR 7.3.5 Eliminating Absolute Values 7.4 Case Study 7.4.1 Data Description and Experiment Setup 7.4.2 Basic Results 7.4.3 Sensitivity Analysis 7.5 Conclusions and Future Works Appendix I Appendix II References 8 Socio-demographic Information Identification 8.1 Introduction 8.2 Problem Definition 8.3 Method 8.3.1 Why Use a CNN? 8.3.2 Proposed Network Structure 8.3.3 Description of the Layers …… 9 Coding for Household Energy Behavior 10 Clustering of Consumption Behavior Dynamics
11 Probabilistic Residential Load Forecasting 12 Aggregated Load Forecasting with Sub-profiles 13 Prospects of Future Research Issues
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