Big Data in Energy Economics

Description

This book combines energy economics and big data modeling analysis in energy conversion and management and comprehensively introduces the relevant theories, key technologies, and application examples of the smart energy economy. With the help of time series big data modeling results, energy economy managers develop reasonable and feasible pricing mechanisms of electricity price and improve the absorption capacity of the power grid. In addition, they also carry out scientific power equipment scheduling and cost–benefit analysis according to the results of data mining, so as to avoid the loss caused by accidental damage of equipment. Energy users adjust their power consumption behavior through the modeling results provided and achieve the effect of energy saving and emission reduction while reasonably reducing the electricity expenditure. This book provides an important reference for professionals in related fields such as smart energy, smart economy, energy Internet, artificial intelligence, energy economics and policy. Preface Contents Nomenclature List of Figures List of Tables 1 Introduction 1.1 Overview of Research Progress in Energy Economics 1.1.1 History of Energy Economics 1.1.2 Framework for Big Data in Energy Economics 1.1.3 Strategies and Measures for the Development of Big Data in China’s Energy Economics 1.1.4 Strategies and Measures for the Development of Big Data in World’s Energy Economics 1.2 Key Technologies of Energy Internet in Energy Economics 1.2.1 Concept of Energy Internet 1.2.2 Reasons for Building a Global Energy Internet 1.2.3 Key Technologies of Energy Internet 1.3 Big Data Demand Analysis for Energy Economics 1.3.1 Summary of Key Technical Tools 1.3.2 Application Scenarios of Big Data Technology 1.4 Scope of This Book References 2 Big Data Analysis of Energy Economics in Oil Market 2.1 Introduction 2.2 Influencing Factors Analysis of Oil Prices 2.2.1 Data Description of Crude Oil Prices Influencing Factors 2.2.2 Correlation Analysis of the Factors Affecting Crude Oil Prices 2.3 Big Data Forecasting of Oil Prices 2.3.1 Base Forecasting Models 2.3.2 Crude Oil Futures and Spot Prices Time Series Forecasting Model 2.3.3 Performance Metrics 2.3.4 Results and Discussions 2.4 Econometric Analysis of Oil Prices 2.4.1 Energy Economic Analysis of Crude Oil Market 2.4.2 Big Data Prediction Technology 2.4.3 Policies and Recommendations 2.5 Conclusions References 3 Big Data Analysis of Energy Economics in Coal Market 3.1 Introduction 3.2 Influencing Factors Analysis of Coal Prices 3.2.1 Data Description of Coal Prices Influencing Factors 3.2.2 Correlation Analysis of the Factors Affecting Coal Prices 3.3 Big Data Forecasting of Coal Prices 3.3.1 The Components of the Proposed Model 3.3.2 Multi-factor Coal Price Hybrid Forecasting Model 3.3.3 Performance Metrics 3.3.4 Results and Discussions 3.4 Econometric Analysis of Coal Prices 3.4.1 Energy Economic Analysis of the Coal Market 3.4.2 Big Data Prediction Technology 3.4.3 Policies and Recommendations 3.5 Conclusions References 4 Big Data Analysis of Energy Economics in Wind Power Market 4.1 Introduction 4.2 Multi-temporal and Spatial Scale Wind Power Big Data Forecasting 4.2.1 Description of Original Wind Dataset 4.2.2 Framework of Wind Power Forecasting Models 4.2.3 Analysis of Wind Power Forecasting Models 4.3 Conversion Efficiency of Wind Power Energy 4.4 Market Economy Analysis of Wind Power Application 4.4.1 Market Economy Analysis of Wind Power Application in China 4.4.2 Market Economy Analysis of Wind Power Application in America 4.4.3 Market Economy Analysis of Wind Power Application in Europe 4.5 Conclusions References 5 Big Data Analysis of Energy Economics in Photovoltaic Power Generation Market 5.1 Introduction 5.2 Big Data Forecasting of Photovoltaic Power Generation 5.2.1 Big Data Processing Engines 5.2.2 Forecasting Strategy and Methods 5.2.3 Forecasting Models 5.3 Photovoltaic Power Consumption by Small and Medium-Sized Users 5.3.1 Dataset Description 5.3.2 Experiments 5.4 Photovoltaic Power Consumption in Urban Public Areas 5.4.1 Dataset Description 5.4.2 Experiments 5.5 Market Economy Analysis of Photovoltaic Systems 5.5.1 Dispatch of Photovoltaic Power Integration 5.5.2 Optimization Model of Photovoltaic Power Integration 5.5.3 Single- and Multi-objective Optimization Algorithms 5.6 Conclusions References 6 Big Data Analysis of Power Market Energy Economics 6.1 Introduction 6.2 Big Data Forecasting of Urban Electricity Price 6.2.1 Electricity Price Forecasting Method Based on Empirical Mode Decomposition and Extreme Learning Machine 6.2.2 Electricity Price Forecasting Method Based on Wavelet Packet Decomposition and Deep Belief Network 6.2.3 Big Data Processing of Electricity Price Based on Empirical Wavelet Transform and Long Short-Term Memory Network 6.3 Correlation Analysis of Urban Energy Consumption and Economic Growth 6.3.1 Grey Correlation Model in the Energy Economy 6.3.2 Grey Correlation Analysis of Economic Growth and Energy Consumption Varieties 6.3.3 Grey Correlation Analysis of Economic Growth and Energy Consumption Industrial Structure 6.4 Metering Charge Adjustment Analysis of City Electricity Prices 6.4.1 Background of the K-means Algorithm for Characteristic Analysis of Electricity Price 6.4.2 Analysis of User Electricity Price Consumption Characteristics Based on the K-means Algorithm 6.4.3 Optimization Design of Residential Stepped Electricity Price 6.5 Conclusions References 7 Big Data Management of Smart City Energy Conservation and Emission Reduction 7.1 Introduction 7.1.1 Background and Introduction 7.1.2 Dataset Description 7.2 Non-intrusive Load Identification of Electrical Equipment 7.2.1 Nonintrusive Load Identification Based on Signal Decomposition 7.2.2 Non-intrusive Load Identification Based on Electrical Switching Event Classification 7.2.3 Non-intrusive Load Identification Based on Multi-label Classification 7.3 Guide to Smart City Electricity Behavior 7.3.1 Smart Grid Planning of a City 7.3.2 Urban Public Electricity Behavior Research 7.4 Analysis of Energy Conservation and Emission Reduction of Smart Cities 7.5 Conclusions References 8 Optimization Analysis of Clean Energy Transformation 8.1 Introduction 8.1.1 Global Status of Clean Energy Development 8.1.2 International Experience in the Transformation of Clean Energy Industry 8.2 Efficiency Analysis of Energy Utilization Under Diversified Development 8.2.1 Evaluation Indexes and Methods of Energy Efficiency 8.2.2 Analysis of Influencing Factors and Mechanism of Energy Efficiency 8.2.3 International Comparative Analysis of Energy Efficiency 8.3 Analysis of Reasonable Energy Consumption Patterns 8.3.1 Challenges Facing Energy Consumption 8.3.2 Analysis of Key Factors Affecting Clean Energy Consumption 8.3.3 Reform Strategy of Clean Energy Consumption Patterns 8.4 Economic Analysis of Clean Energy Transformation 8.4.1 Mechanisms for Developing Clean Energy to Affect Economic Growth 8.4.2 Ways to Promote a Low-Carbon Economy Based on Clean Energy 8.5 Conclusions References 9 Global Energy Internet Green and Low-Carbon Energy Economic Innovation 9.1 Introduction 9.2 Reform and Innovation of the New Energy System Under the Energy Internet 9.2.1 Comparison of Conventional Energy System and New Energy System 9.2.2 Production in New Energy System 9.2.3 Supply and Marketing in New Energy System 9.3 Energy Saving and Emission Reduction Under the Energy Internet 9.3.1 Energy Saving and Emission Reduction in Production Process 9.3.2 Energy Saving and Emission Reduction in Supply and Marketing Process 9.4 Healthy Construction of the Ecological Environment Under the Energy Internet 9.4.1 Land Ecology and Photovoltaic Power 9.4.2 Hydropower and Ecology 9.4.3 Biological Energy and Ecology 9.5 Conclusions References