Kooperativer Bibliotheksverbund

UID:

Format: 1 Online-Ressource (XXII, 258 p. 97 illus., 92 illus. in color)

Edition: 1st ed. 2022

ISBN: 9789811689659

Series Statement: Management for Professionals

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1689-64-2

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1689-66-6

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-1689-67-3

Language: English

DOI: 10.1007/978-981-16-8965-9

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 7, 486, 20 Seiten , 图

Edition: 第1版

Original writing title: 干旱环境下古代壁画保护成套技术集成与应用示范研究

Original writing person/organisation: 陈港泉

Original writing publisher: 北京 : 科学出版社

ISBN: 9787030624802

Note: 附参考文献

Language: Chinese

UID:

Format: 1 Online-Ressource (275 Seiten)

ISBN: 9789811689659

Series Statement: Management for Professionals Series

Note: Description based on publisher supplied metadata and other sources , Intro -- 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

Additional Edition: Erscheint auch als Druck-Ausgabe Liu, Hui Big Data in Energy Economics Singapore : Springer,c2022 ISBN 9789811689642

Language: English

UID:

Format: 136 Seiten

ISBN: 9780367902704

Content: Speaking Out: Issues and Controversies is an advanced Chinese language textbook that explores topics such as human nature, moral values, mass consumption, Western influences, and technological innovation. In presenting subjects that reflect major concerns in contemporary China, the book invites students to reflect upon the forces shaping modern Chinese society. This textbook presents ten lessons in five units entitled "Constancy and Change," "Joy and Sorrow," "Right and Wrong," "Chinese Tradition and Western Influence," and "New and Old." These pairs of opposites conjure up an ever-changing world of ebb and flow, a world that stimulates learners' imaginations and arouses their enthusiasm for open dialogue and lively discussion. Concise in language and with lessons in both simplified and traditional characters, the textbook is a valuable aid for university students interested in passing the HSK Level VI or attaining ACTFL advanced-level proficiency.

Note: Englisch ; Chinesisch

Language: English

Keywords: Chinesisch ; Leseverstehen ; China ; Gesellschaft ; Landeskunde ; Aufgabensammlung