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Big Data in Energy Economics

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Big Data in Energy Economics/ by Hui Liu, Nikolaos Nikitas, Yanfei Li, Rui Yang.
Author:	Liu, Hui.
other author:	Nikitas, Nikolaos.
Description:	XXII, 258 p. 97 illus., 92 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Power resources. -
Online resource:	https://doi.org/10.1007/978-981-16-8965-9
ISBN:	9789811689659

Big Data in Energy Economics
Liu, Hui.

Big Data in Energy Economics[electronic resource] /by Hui Liu, Nikolaos Nikitas, Yanfei Li, Rui Yang. - 1st ed. 2022. - XXII, 258 p. 97 illus., 92 illus. in color.online resource. - Management for Professionals,2192-810X. - Management for Professionals,.

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. .

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.

ISBN: 9789811689659

Standard No.: 10.1007/978-981-16-8965-9doiSubjects--Topical Terms:

554735
Power resources.

LC Class. No.: HD9502-9502.5

Dewey Class. No.: 333.79

Big Data in Energy Economics
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