Big Data in Energy Economics

ISBN-13: 9789811689642 / Angielski / Twarda / 2022 / 282 str.

Hui Liu; Nikolaos Nikitas; Yanfei Li

Spis treści

Chapter 1 Introduction

1.1 Overview of Research Progress in Energy Economics

1.2 Key Technologies of Energy Internet in Energy Economics

1.3 Big Data Demand Analysis for Energy Economics

1.4 Scope of This Book

1.5 References

Chapter 2 Big Data Analysis of Energy Economics in Oil Market 

2.1 Introduction

2.2 Influencing Factors Analysis of Oil Prices

2.3 Big Data Forecasting of Oil Prices

2.4 Econometric Analysis of Oil Prices

2.5 Conclusions

2.6 References

Chapter 3 Big Data Analysis of Energy Economics in Coal Market

3.1 Introduction

3.2 Influencing Factors Analysis of Coal Prices

3.3 Big Data Forecasting of Coal Prices

3.4 Econometric Analysis of Coal Prices

3.5 Conclusions

3.6 References

Chapter 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

4.3 Conversion Efficiency of Wind Power Energy 

4.4 Market Economy Analysis of Wind Power Application

4.5 Conclusions

4.6 References

Chapter 5 Big Data Analysis of Energy Economics in Photovoltaic Power Generation Market

5.1 Introduction

5.2 Big Data Forecasting of Photovoltaic Power Generation

5.3 Photovoltaic Power Consumption by Small and Medium-sized Users

5.4 Photovoltaic Power Consumption in Urban Public Areas

5.5 Market Economy Analysis of Photovoltaic Systems

5.6 Conclusions

5.7 References

Chapter 6 Big Data Analysis of Energy Economics in Power Market 

6.1 Introduction

6.2 Big Data Forecasting of Urban Electricity Prices

6.3 Correlation Analysis of Urban Energy Consumption and Economic Growth

6.4 Metering Charge Adjustment Analysis of City Electricity Prices

6.5 Conclusions

6.6 References

Chapter 7 Big Data Management of Energy Conservation and Emission Reduction in Smart Cities

7.1 Introduction

7.2 Non-intrusive Identification of Smart Electrical Equipment

7.3 Electricity Consumption Behavior Guidance in Smart Cities 

7.4 Effectiveness Analysis of Energy Conservation and Emission Reduction in Smart Cities

7.5 Conclusions

7.6 References

Chapter 8 Optimization Analysis of Clean Energy Transformation

8.1 Introduction

8.2 Efficiency Analysis of Energy Utilization Under Diversified Development

8.3 Analysis of Reasonable Energy Consumption Patterns

8.4 Economic Analysis of Clean Energy Transformation

8.5 Conclusions

8.6 References

Chapter 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

9.3 Energy Saving and Emission Reduction Under the Energy Internet

9.4 Healthy Construction of the Ecological Environment Under the Energy Internet

9.5 Conclusions

9.6 References

O autorze

Dr. Hui Liu is a Full Professor of Artificial Intelligence, Smart Cities and Smart Energy at Central South University (CSU), China. Prof. Liu is the director of Institute of Artificial Intelligence and Robotics at CSU. He received double Ph.D degrees from Central South University (China) in 2011 and University of Rostock (Germany) in 2013, respectively. He received habilitation degree from University of Rostock in 2016. He was appointed as the BMBF junior group leader by the Ministry of Education and Research of Germany at University of Rostock since January, 2015 until December 2016.

Dr. Nikolaos Nikitas is an Associate Professor in Structural Engineering, Data Sciences and Wind Energy at University of Leeds, UK. Prof. Nikitas is the data centric engineering group leader at The Alan Turing Institute, UK. He received double Ph.D degrees from The University of Edinburgh in 2008 and University of Bristol in 2011, respectively. 

Dr. Yanfei Li is an Associate Professor in Artificial Intelligence, Smart Agriculture and Smart Energy at Hunan Agricultural University (HAU), China. Prof. Li is the director of Institute of Artificial Intelligence at HAU. She received Ph.D degree from University of Rostock (Germany) in 2014 then worked as a postdoctoral fellow at University of Rostock in 2015.

Mr. Rui Yang is a Ph.D Candidate in Smart Energy Systems at Central South University (CSU), China. 

Blurb

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.