- 电力市场大数据分析(英文版)
- - 作者：陈启鑫
  - 出版社：科学
  - ISBN：9787030715166
  - 出版日期：2022/01/01
  - 页数：284
- 售价：63.2

内容大纲
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。
作者介绍
目录
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  Feature Engineering
    7.3.3  Regression Model Selection and Parameter Tuning
    7.3.4  Model Stacking with Robust Regression
    7.3.5  Metrics
  7.4  Case Study
    7.4.1  Model Selection Results
    7.4.2  Componential Results
    7.4.3  Stacking Results (Overall Improvements)
    7.4.4  Error Distribution Analysis
  7.5  Conclusion
  References
8  Economic Impact of Price Forecasting Error
  8.1  Introduction
  8.2  General Bidding Models
    8.2.1  Deterministic Bidding Model
    8.2.2  Stochastic Bidding Model
  8.3  Methodology and Framework
    8.3.1  Forecasting Error Modeling
    8.3.2  Multiparametric Linear Programming (MPLP)Theory
    8.3.3  Error Impact Formulation
    8.3.4  Overall Framework
  8.4  Case Study
    8.4.1  Measurement of STPF Error Level
    8.4.2  Case1: LSE with Demand Response Programs
    8.4.3  Case 2: LSE with ESS
    8.4.4  Case3: Stochastic LSE Bidding Model
    8.4.5  TimeConsumption
  8.5  Conclusions and FutureWork
  References
9  LMP Forecasting and FTR Speculation
  9.1  Introduction
  9.2  Stochastic OptimizationModel
    9.2.1  Model of FTR Portfolio Construction Problem
    9.2.2  Scenario-Based Stochastic Optimization Model
  9.3  Data-Dnven Framework

  9.4  Methodology
    9.4.1  Clustering
    9.4.2  Mid-Term Probabilistic Forecasting
    9.4.3  Copulas for Dependence Modeling
    9.4.4  Training and Evaluation Timeline
    9.4.5  Scenario Generation
  9.5  Case Study
    9.5.1  Data Description
    9.5.2  Comparison Methods
    9.5.3  Statistical Validation of Quantile Regression
    9.5.4  Scenario Quality Evaluation
    9.5.5  Impact of Node Reduction with Clustering
    9.5.6  Revenue and Risk Estimation
    9.5.7  Sensitivity Analysis on the Number of Clusters
  9.6  Conclusion
  References
Part Ⅲ Market Bidding Behavior Analysis
10  Pattern Extraction for Bidding Behaviors
  10.1  Introduction
  10.2  Assumptions and Proposed Framework
    10.2.1  Model Assumptions
    10.2.2  Bidding Data Format
    10.2.3  Data-Driven Analysis Framework
  10.3  Data Standardization Processing
    10.3.1  Filtering Available Capacities
    10.3.2  Sampling Bidding Curves
    10.3.3  Unifying Data Length
    10.3.4  Clipping Extreme Prices
  10.4  Adaptive Clustering of Bidding Behaviors
    10.4.1  Distance Measurement
    10.4.2  K-Medoids Clustering
    10.4.3  Adaptive Clustering Procedure
    10.4.4  Clustering Algorithm
  10.5  AEM Data Description
    10.5.1  Description of Market Participants
    10.5.2  Description of Bidding Data
  10.6  Bidding Pattern Analysis
    10.6.1  Parameter Setting
    10.6.2  Bidding Patterns of DUs by Fuel Type
    10.6.3  Comparison of Similar DUs
    10.6.4  Discussion
  10.7  Feature Analysis on Bids
    10.7.1  Discrete Aggregation Feature
    10.7.2  Probability Distribution Feature
    10.7.3  Time Distribution Feature
  10.8  Conclusions
  References
11  Aggregated Supply Curves Forecasting
  11.1  Introduction
  11.2  Market and Framework

    11.2.1  Market Descriptions
    11.2.2  Forecasting Framework
  11.3  Data Integration and Feature Extraction
    11.3.1  Data Integration
    11.3.2  Feature Extraction
  11.4  ASC Forecasting
    11.4.1  LSTM Model
    11.4.2  Influencing Factors
    11.4.3  Training and Forecasting
    11.4.4  Evaluation Criteria
  11.5  Case Study
    11.5.1  Dataset Description
    11.5.2  Feature Extraction
    11.5.3  ASC Forecasting
    11.5.4  Calculation Information
    11.5.5  Methods Comparison
  11.6  Conclusion
  References
12  Learning Individual Offering Strategy
  12.1  Introduction
  12.2  Data-Driven Market Simulation Framework
    12.2.1  Market Assumptions
    12.2.2  Offering Data Clustering and Indexing
  12.3  Individual Offering Strategy Learning
    12.3.1  MFNN Model Structure
    12.3.2  MFNN Model Inputs
    12.3.3  MFNN Model Training
    12.3.4  DNN-Based Model Structure
  12.4  Market Clearing Simulation
  12.5  Case Study
    12.5.1  Basic Data
    12.5.2  Individual Offering Behavior Forecasting
    12.5.3  Market Simulation
    12.5.4  Comparison with Current Price Forecasting Methods
    12.5.5  Calculation Efficiency
  12.6  Conclusions
  References
13  Reward Function Identification of GENCOs
  13.1  Introduction
  13.2  Assumptions and Framework
    13.2.1  Market Assumptions
    13.2.2  Data-Driven Framework
  13.3  Bidding Decision Process Formulation
    13.3.1  Markov Decision Process in Wholesale Markets
    13.3.2  Reinforcement Learning Process
    13.3.3  Bidding Data Integration
  13.4  Reward Function Identification
    13.4.1  Deep Inverse Reinforcement Learning Algorithm
    13.4.2  Discretization Methods for States and Actions
  13.5  Bidding Behavior Simulation

    13.5.1  DQN-Based Bidding Simulation Model
    13.5.2  Value Function and Q-Network
  13.6  Case Study
    13.6.1  Dataset Description
    13.6.2  Parameter Setting
    13.6.3  Reward Function Identification
    13.6.4  Bidding Behavior Simulation
  13.7  Conclusions
  References

内容大纲

作者介绍

目录

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