Big Data in Energy Economics

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.

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.

Evaluates science technologies by a large number of simulation experiments

Preface.- Chapter 1 Introduction.- Chapter 2 Big Data Analysis of Energy Economics in Oil Market.- Chapter 2 Big Data Analysis of Energy Economics in Oil Market.- Chapter 3 Big Data Analysis of Energy Economics in Coal Market.- Chapter 4 Big Data Analysis of Energy Economics in Wind Power Market.- Chapter 5 Big Data Analysis of Energy Economics in Photovoltaic Power Generation Market.- Chapter 6 Big Data Analysis of Power Market Energy Economics.- Chapter 7 Big Data Management of Smart City Energy Conservation and Emission Reduction.- Chapter 8 Optimization Analysis of Clean Energy Transformation.- Chapter 9 Global Energy Internet Green and Low-Carbon Energy Economic Innovation.