Smart Meter Data Analytics: Electricity Consumer Behavior Modeling, Aggregation, and Forecasting

✍ Scribed by Yi Wang, Qixin Chen, Chongqing Kang

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 306
• Category: Library

✦ Synopsis

This book aims to make the best use of fine-grained smart meter data to process and translate them into actual information and incorporated into consumer behavior modeling and distribution system operations. It begins with an overview of recent developments in smart meter data analytics. Since data management is the basis of further smart meter data analytics and its applications, three issues on data management, i.e., data compression, anomaly detection, and data generation, are subsequently studied. The following works try to model complex consumer behavior. Specific works include load profiling, pattern recognition, personalized price design, socio-demographic information identification, and household behavior coding. On this basis, the book extends consumer behavior in spatial and temporal scale. Works such as consumer aggregation, individual load forecasting, and aggregated load forecasting are introduced. We hope this book can inspire readers to define new problems, apply novel methods, and obtain interesting results with massive smart meter data or even other monitoring data in the power systems.

✦ Table of Contents

Foreword
Preface
Acknowledgements
Contents
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
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
8.3.4 Reducing Overfitting
8.3.5 Training Method
8.4 Performance Evaluation and Comparisons
8.4.1 Performance Evaluation
8.4.2 Competing Methods
8.5 Case Study
8.5.1 Data Description
8.5.2 Basic Results
8.5.3 Comparative Analysis
8.6 Conclusions
References
9 Coding for Household Energy Behavior
9.1 Introduction
9.2 Basic Idea and Framework
9.3 Load Profile Clustering
9.3.1 GMM-Based Typical Load Profile Extraction
9.3.2 X-Means-Based Load Profile Clustering
9.4 Socioeconomic Genes Identification Method
9.4.1 Socioeconomic Information Classification
9.4.2 The Concept of Socioeconomic Genes
9.4.3 Socioeconomic Genes Evaluation Indicators
9.4.4 Socioeconomic Gene Search Method
9.5 Load Profile Prediction
9.6 Case Studies
9.6.1 Consumer Load Profile Classification
9.6.2 Socioeconomic Gene Search Result
9.6.3 Consumer Load Profile Prediction
9.7 Conclusions
References
10 Clustering of Consumption Behavior Dynamics
10.1 Introduction
10.2 Basic Methodology
10.2.1 Data Normalization
10.2.2 SAX for Load Curves
10.2.3 Time-Based Markov Model
10.2.4 Distance Calculation
10.2.5 CFSFDP Algorithm
10.3 Distributed Algorithm for Large Data Sets
10.3.1 Framework
10.3.2 Local Modeling-Adaptive k-Means
10.3.3 Global Modeling-Modified CFSFDP
10.4 Case Studies
10.4.1 Description of the Data Set
10.4.2 Modeling Consumption Dynamics for Each Customer
10.4.3 Clustering for Full Periods
10.4.4 Clustering for Each Adjacent Periods
10.4.5 Distributed Clustering
10.5 Potential Applications
10.6 Conclusions
References
11 Probabilistic Residential Load Forecasting
11.1 Introduction
11.2 Pinball Loss Guided LSTM
11.2.1 LSTM
11.2.2 Pinball Loss
11.2.3 Overall Networks
11.3 Implementations
11.3.1 Framework
11.3.2 Data Preparation
11.3.3 Model Training
11.3.4 Probabilistic Forecasting
11.4 Benchmarks
11.4.1 QRNN
11.4.2 QGBRT
11.4.3 LSTM+E
11.5 Case Studies
11.5.1 Data Description
11.5.2 Residential Load Forecasting Results
11.5.3 SME Load Forecasting Results
11.6 Conclusions
References
12 Aggregated Load Forecasting with Sub-profiles
12.1 Introduction
12.2 Load Forecasting with Different Aggregation Levels
12.2.1 Variance of Aggregated Load Profiles
12.2.2 Scaling Law
12.3 Clustering-Based Aggregated Load Forecasting
12.3.1 Framework
12.3.2 Numerical Experiments
12.4 Ensemble Forecasting for the Aggregated Load
12.4.1 Proposed Methodology
12.4.2 Case Study
12.5 Conclusions
References
13 Prospects of Future Research Issues
13.1 Big Data Issues
13.2 New Machine Learning Technologies
13.3 New Business Models in Retail Market
13.4 Transition of Energy Systems
13.5 Data Privacy and Security
References