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电力市场大数据分析（英文版）
作者：陈启鑫 等 著
出版社：科学出版社
出版时间：2022-10-01
ISBN：9787030715166
定价：¥158.00
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内容简介
《电力市场大数据分析=Data Analytics in Power Markets：英文》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。《电力市场大数据分析=Data Analytics in Power Markets：英文》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。 《电力市场大数据分析=Data Analytics in Power Markets：英文》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。
作者简介
暂缺《电力市场大数据分析（英文版）》作者简介
目录
Contents
1 Introduction to Power Market Data 1
1.1 Overview of Electricity Markets 1
1.2 Organization and Data Disclosure of Electricity Market 4
1.2.1 Transaction Data 5
1.2.2 Price Data 7
1.2.3 Supply and Demand Data 7
1.2.4 System Operation Data 8
1.2.5 Forecast Data 8
1.2.6 Confidential Data 9
1.3 Conclusions 9
References 9
PartⅠ Load Modeling and Forecasting
2 Load Forecasting with Smart Meter Data 13
2.1 Introduction 13
2.2 Framework 14
2.3 Ensemble Learning for Probabilistic Forecasting 16
2.3.1 Quantile Regression Averaging 17
2.3.2 Factor Quantile Regression Averaging 18
2.3.3 LASSO Quantile Regression Averaging 18
2.3.4 Quantile Gradient Boosting Regression Tree 19
2.3.5 Rolling Window-Based Forecasting 20
2.4 Case Study 20
2.4.1 Experimental Setups 2
2.4.2 Evaluation Criteria 21
2.4.3 Experimental Results 22
2.5 Conclusions 24
References 24
3 Load Data Cleaning and Forecasting 27
3.1 Introduction 27
3.2 Characteristics of Load Profiles 29
3.2.1 Low-Rank Property of Load Profiles 29
3.2.2 Bad Data in Load Profiles 30
3.3 Methodology 31
3.3.1 Framework 31
3.3.2 Singular Value Thresholding (SVT) 32
3.3.3 Quantile RF Regression 34
3.3.4 Load Forecasting 35
3.4 Evaluation Criteria 35
3.4.1 Data Cleaning-Based Criteria 35
3.4.2 Load Forecasting-Based Criteria 35
3.5 Case Study 36
3.5.1 Result of Data Cleaning 36
3.5.2 Day Ahead Point Forecast 37
3.5.3 Day Ahead Probabilistic Forecast 38
3.6 Conclusions 40
References 40
4 Monthly Electricity Consumption Forecasting 43
4.1 Introduction 43
4.2 Framework 46
4.2.1 Data Collection and Treatment 46
4.2.2 SVECM Forecasting 47
4.2.3 Self-adaptive Screening 48
4.2.4 Novelty and Characteristics of SAS-SVECM 48
4.3 Data Collection and Treatment 48
4.3.1 Data Collection and Tests 49
4.3.2 Seasonal Adjustments Based on X-12-ARIMA 49
4.4 SVECM Forecasting 49
4.4.1 VECM Forecasting 49
4.4.2 Time Series Extrapolation Forecasting 52
4.5 Self-adaptive Screening 53
4.5.1 Influential EEF Identification 53
4.5.2 Influential EEF Grouping 53
4.5.3 Forecasting Performance Evaluation Considering Different EEF Groups 55
4.6 Case Study 56
4.6.1 Basic Data and Tests 56
4.6.2 Electricity Consumption Forecasting Performance Without SAS 58
4.6.3 EC Forecasting Performance with SAS 61
4.6.4 SAS-SVECM Forecasting Comparisons with Other Forecasting Methods 65
4.7 Conclusions 67
References 67
5 Probabilistic Load Forecasting 71
5.1 Introduction 71
5.2 Data and Model 73
5.2.1 Load Dataset Exploration 73
5.2.2 Linear Regression Model Considering Recency-Effects 73
5.3 Pre-Lasso Based Feature Selection 76
5.4 Sparse Penalized Quantile Regression (Quantile-Lasso) 77
5.4.1 Problem Formulation 77
5.4.2 ADMM Algorithm 78
5.5 Implementation 80
5.6 Case Study 81
5.6.1 Experiment Setups 81
5.6.2 Results 82
5.7 Concluding Remarks 86
References 86
Part Ⅱ Electricity Price Modeling and Forecasting
6 Subspace Characteristics of LMP Data 91
6.1 Introduction 91
6.2 Model and Distribution of LMP 93
6.3 Methodology
6.3.1 Problem Formulation 96
6.3.2 Basic Framework 97
6.3.3 Principal Component Analysis 98
6.3.4 Recursive Basis Search (Bottom-Up) 98
6.3.5 Hyperplane Detection (Top-down) 100
6.3.6 Short Summary 103
6.4 Case Study 103
6.4.1 Case 1: IEEE 30-Bus System 104
6.4.2 Case 2: IEEE 118-Bus System 106
6.4.3 Case 3: Illinois 200-Bus System 106
6.4.4 Case 4: Southwest Power Pool (SPP) 107
6.4.5 Time Consumption 108
6.5 Discussion and Conclusion 110
6.5.1 Discussion on Potential Applications 110
6.5.2 Conclusion 110
References 111
7 Day-Ahead Electricity Price Forecasting 113
7.1 Introduction 113
7.2 Problem Formulation 116
7.2.1 Decomposition of LMP 116
7.2.2 Short-Term Forecast for Each Component 117
7.2.3 Summation and Stacking of Individual Forecasts 118
7.3 Methodology 119
7.3.1 Framework 119
7.3.2 Feature Engineering 121
7.3.3 Regression Model Selection and Parameter Tuning 122
7.3.4 Model Stacking with Robust Regression 123
7.3.5 Metrics 124
7.4 Case Study 124
7.4.1 Model Selection Results 125
7.4.2 Componential Results 126
7.4.3 Stacking Results (Overall Improvements) 128
7.4.4 Error Distribution Analysis 129
7.5 Conclusion 132
References 132
8 Economic Impact of Price Forecasting Error 135
8.1 Introduction 135
8.2 General Bidding Models 137
8.2.1 Deterministic Bidding Model 138
8.2.2 Stochastic Bidding Model 139
8.3 Methodology and Framework 141
8.3.1 Forecasting Error Modeling 141
8.3.2 Multiparametric Linear Programming (MPLP)Theory 141
8.3.3 Error Impact Formulation 142
8.3.4 Overall Framework 144
8.4 Case Study 145
8.4.1 Measurement of STPF Error Level 145
8.4.2 Case 1: LSE with Deman
1 Introduction to Power Market Data 1
1.1 Overview of Electricity Markets 1
1.2 Organization and Data Disclosure of Electricity Market 4
1.2.1 Transaction Data 5
1.2.2 Price Data 7
1.2.3 Supply and Demand Data 7
1.2.4 System Operation Data 8
1.2.5 Forecast Data 8
1.2.6 Confidential Data 9
1.3 Conclusions 9
References 9
PartⅠ Load Modeling and Forecasting
2 Load Forecasting with Smart Meter Data 13
2.1 Introduction 13
2.2 Framework 14
2.3 Ensemble Learning for Probabilistic Forecasting 16
2.3.1 Quantile Regression Averaging 17
2.3.2 Factor Quantile Regression Averaging 18
2.3.3 LASSO Quantile Regression Averaging 18
2.3.4 Quantile Gradient Boosting Regression Tree 19
2.3.5 Rolling Window-Based Forecasting 20
2.4 Case Study 20
2.4.1 Experimental Setups 2
2.4.2 Evaluation Criteria 21
2.4.3 Experimental Results 22
2.5 Conclusions 24
References 24
3 Load Data Cleaning and Forecasting 27
3.1 Introduction 27
3.2 Characteristics of Load Profiles 29
3.2.1 Low-Rank Property of Load Profiles 29
3.2.2 Bad Data in Load Profiles 30
3.3 Methodology 31
3.3.1 Framework 31
3.3.2 Singular Value Thresholding (SVT) 32
3.3.3 Quantile RF Regression 34
3.3.4 Load Forecasting 35
3.4 Evaluation Criteria 35
3.4.1 Data Cleaning-Based Criteria 35
3.4.2 Load Forecasting-Based Criteria 35
3.5 Case Study 36
3.5.1 Result of Data Cleaning 36
3.5.2 Day Ahead Point Forecast 37
3.5.3 Day Ahead Probabilistic Forecast 38
3.6 Conclusions 40
References 40
4 Monthly Electricity Consumption Forecasting 43
4.1 Introduction 43
4.2 Framework 46
4.2.1 Data Collection and Treatment 46
4.2.2 SVECM Forecasting 47
4.2.3 Self-adaptive Screening 48
4.2.4 Novelty and Characteristics of SAS-SVECM 48
4.3 Data Collection and Treatment 48
4.3.1 Data Collection and Tests 49
4.3.2 Seasonal Adjustments Based on X-12-ARIMA 49
4.4 SVECM Forecasting 49
4.4.1 VECM Forecasting 49
4.4.2 Time Series Extrapolation Forecasting 52
4.5 Self-adaptive Screening 53
4.5.1 Influential EEF Identification 53
4.5.2 Influential EEF Grouping 53
4.5.3 Forecasting Performance Evaluation Considering Different EEF Groups 55
4.6 Case Study 56
4.6.1 Basic Data and Tests 56
4.6.2 Electricity Consumption Forecasting Performance Without SAS 58
4.6.3 EC Forecasting Performance with SAS 61
4.6.4 SAS-SVECM Forecasting Comparisons with Other Forecasting Methods 65
4.7 Conclusions 67
References 67
5 Probabilistic Load Forecasting 71
5.1 Introduction 71
5.2 Data and Model 73
5.2.1 Load Dataset Exploration 73
5.2.2 Linear Regression Model Considering Recency-Effects 73
5.3 Pre-Lasso Based Feature Selection 76
5.4 Sparse Penalized Quantile Regression (Quantile-Lasso) 77
5.4.1 Problem Formulation 77
5.4.2 ADMM Algorithm 78
5.5 Implementation 80
5.6 Case Study 81
5.6.1 Experiment Setups 81
5.6.2 Results 82
5.7 Concluding Remarks 86
References 86
Part Ⅱ Electricity Price Modeling and Forecasting
6 Subspace Characteristics of LMP Data 91
6.1 Introduction 91
6.2 Model and Distribution of LMP 93
6.3 Methodology
6.3.1 Problem Formulation 96
6.3.2 Basic Framework 97
6.3.3 Principal Component Analysis 98
6.3.4 Recursive Basis Search (Bottom-Up) 98
6.3.5 Hyperplane Detection (Top-down) 100
6.3.6 Short Summary 103
6.4 Case Study 103
6.4.1 Case 1: IEEE 30-Bus System 104
6.4.2 Case 2: IEEE 118-Bus System 106
6.4.3 Case 3: Illinois 200-Bus System 106
6.4.4 Case 4: Southwest Power Pool (SPP) 107
6.4.5 Time Consumption 108
6.5 Discussion and Conclusion 110
6.5.1 Discussion on Potential Applications 110
6.5.2 Conclusion 110
References 111
7 Day-Ahead Electricity Price Forecasting 113
7.1 Introduction 113
7.2 Problem Formulation 116
7.2.1 Decomposition of LMP 116
7.2.2 Short-Term Forecast for Each Component 117
7.2.3 Summation and Stacking of Individual Forecasts 118
7.3 Methodology 119
7.3.1 Framework 119
7.3.2 Feature Engineering 121
7.3.3 Regression Model Selection and Parameter Tuning 122
7.3.4 Model Stacking with Robust Regression 123
7.3.5 Metrics 124
7.4 Case Study 124
7.4.1 Model Selection Results 125
7.4.2 Componential Results 126
7.4.3 Stacking Results (Overall Improvements) 128
7.4.4 Error Distribution Analysis 129
7.5 Conclusion 132
References 132
8 Economic Impact of Price Forecasting Error 135
8.1 Introduction 135
8.2 General Bidding Models 137
8.2.1 Deterministic Bidding Model 138
8.2.2 Stochastic Bidding Model 139
8.3 Methodology and Framework 141
8.3.1 Forecasting Error Modeling 141
8.3.2 Multiparametric Linear Programming (MPLP)Theory 141
8.3.3 Error Impact Formulation 142
8.3.4 Overall Framework 144
8.4 Case Study 145
8.4.1 Measurement of STPF Error Level 145
8.4.2 Case 1: LSE with Deman
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