Machine Learning Technologies on Energy Economics and Finance – Energy and Sustainable Analytics, Volume I & II  

- By Springer

Publication in Studies in International Series in Operations Research & Management Science

Indexed by Web of Science (WoS)

Submission Deadline: 15 July 2024

Publication Deadline: 15 December 2024

Submission Link:  Click Here to Submit

Scope

The proposed book will present the significance and application of machine learning algorithms for the development of the energy sector. This book will also highlight the introduction of energy economics and finance; consider all necessary innovative factors that contribute to the development of energy and sustainable analytics.  Energy economics basically focuses on the production and consumption of energy under socioeconomic perspective and Energy finance is a sub-field of energy economic. This proposed book will cover all factors that have an impact on fossil fuels such as market structures, regulatory frame works, environmental impacts, and global energy market. 

This book will be beneficial to academics, practitioners, financial managers, stakeholders, government, and policy makers who are looking for ways the advancement of energy systems, reducing energy costs, increasing revenue for economic growth. Renewable Energy Technologies (RETs) place a significant role on the development of energy economics and finance which is an alternative to fossil fuel.  Thus, the purpose of this proposed book is to provide the prediction of energy prices, crude oil prices, electricity prices, fuel wood prices, solar prices, natural gas prices, and show the role of RETs on energy economic sector.


Keywords

Machine learning; Data Analytics; Deep learning; Energy Analytics; Energy consumption and production; Energy price; Climate finance; Global energy market; Sustainable finance; CO2 emission 


Topics