本书以电力市场领域近年来的研究工作成果为基础，力图系统性地介绍电力市场中的数据价值挖掘方法以支撑市场组织者和市场参与者的决策问题。本书围绕电力市场中的公开数据和机器学习方法理论与应用展开，结合电力市场规则和物理特征，期望解决市场规则解析和数据结构化两大核心难点，并从负荷与电价预测、报价行为解析、金融衍生品投机等方面，构建了电力市场数据分析理论和技术方法体系。
全书共13章，第1章介绍了世界各地的电力市场数据概况。除第1章外，剩余内容分为三部分。第一部分为负荷建模与预测，包括了基于智能电表数据的负荷预测方法等。第二部分为电价建模与预测，包括了节点电价数据的子空间特性建模等。第三部分为市场投标行为分析，包括了机组投标行为的特征提取方法等。

Contents
1 Introduction to Power Market Data
1.1 Overview of Electricity Markets
1.2 Organization and Data Disclosure of Electricity Market
1.2.1 Transaction Data
1.2.2 Price Data
1.2.3 Supply and Demand Data
1.2.4 System Operation Data
1.2.5 Forecast Data
1.2.6 Confidential Data
1.3 Conclusions
References
PartⅠLoad Modeling and Forecasting
2 Load Forecasting with Smart Meter Data
2.1 Introduction
2.2 Framework
2.3 Ensemble Learning for Probabilistic Forecasting
2.3.1 Quantile Regression Averaging
2.3.2 Factor Quantile Regression Averaging
2.3.3 LASSO Quantile Regression Averaging
2.3.4 Quantile Gradient Boosting Regression Tree
2.3.5 Rolling Window-Based Forecasting
2.4 Case Study
2.4.1 Experimental Setups
2.4.2 Evaluation Criteria
2.4.3 Experimental Results
2.5 Conclusions
References
3 Load Data Cleaning and Forecasting
3.1 Introduction
3.2 Characteristics of Load Profiles
3.2.1 Low-Rank Property of Load Profiles
3.2.2 Bad Data in Load Profiles
3.3 Methodology
3.3.1 Framework
3.3.2 Singular Value Thresholding (SVT)
3.3.3 Quantile RF Regression
3.3.4 Load Forecasting
3.4 Evaluation Criteria
3.4.1 Data Cleaning-Based Criteria
3.4.2 Load Forecasting-Based Criteria
3.5 Case Study
3.5.1 Result of Data Cleaning
3.5.2 Day Ahead Point Forecast
3.5.3 Day Ahead Probabilistic Forecast
3.6 Conclusions
References
4 Monthly Electricity Consumption Forecasting
4.1 Introduction
4.2 Framework
4.2.1 Data Collection and Treatment
4.2.2 SVECM Forecasting
4.2.3 Self-adaptive Screening
4.2.4 Novelty and Characteristics of SAS-SVECM
4.3 Data Collection and Treatment
4.3.1 Data Collection and Tests
4.3.2 Seasonal Adjustments Based on X-12-ARIMA
4.4 SVECM Forecasting
4.4.1 VECM Forecasting
4.4.2 Time Series Extrapolation Forecasting
4.5 Self-adaptive Screening
4.5.1 Influential EEF Identification
4.5.2 Influential EEF Grouping
4.5.3 Forecasting Performance Evaluation Considering Different EEF Groups
4.6 Case Study
4.6.1 Basic Data and Tests
4.6.2 Electricity Consumption Forecasting Performance Without SAS
4.6.3 EC Forecasting Performance with SAS
4.6.4 SAS-SVECM Forecasting Comparisons with Other Forecasting Methods
4.7 Conclusions
References
5 Probabilistic Load Forecasting
5.1 Introduction
5.2 Data and Model
5.2.1 Load Dataset Exploration
5.2.2 Linear Regression Model Considering Recency-Effects
5.3 Pre-Lasso BasedFeature Selection
5.4 Sparse PenalizedQuantileRegression (Quantile-Lasso)
5.4.1 Problem Formulation
5.4.2 ADMM Algorithm
5.5 Implementation
5.6 Case Study
5.6.1 Experiment Setups
5.6.2 Results
5.7 Concluding Remarks
References
Part ⅡElectricity Price Modeling and Forecasting
6 Subspace Characteristics of LMP Data
6.1 Introduction
6.2 Model and Distribution of LMP
6.3 Methodology
6.3.1 Problem Formulation
6.3.2 Basic Framework
6.3.3 Principal Component Analysis
6.3.4 Recursive Basis Search (Bottom-Up)
6.3.5 Hyperplane Detection (Top-down)
6.3.6 Short Summary
6.4 Case Study
6.4.1 Case 1: IEEE 30-Bus System
6.4.2 Case 2: IEEE 118-Bus System
6.4.3 Case 3: Illinois 200-Bus System
6.4.4 Case 4: Southwest Power Pool (SPP)
6.4.5 Time Consumption
6.5 Discussion and Conclusion
6.5.1 Discussion on Potential Applications
6.5.2 Conclusion
References
7 Day-Ahead Electricity Price Forecasting
7.1 Introduction
7.2 Problem Formulation
7.2.1 Decomposition of LMP
7.2.2 Short-Term Forecast for Each Component
7.2.3 Summation and Stacking of Individual Forecasts
7.3 Methodology
7.3.1 Framework
7.3.2 Featu