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Machine learning technologies on energy economics and financeenergy and sustainable analytics.Volume 1 /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine learning technologies on energy economics and financeedited by Mohammad Zoynul Abedin, Wang Yong.
Reminder of title:	energy and sustainable analytics.
other author:	Abedin, Mohammad Zoynul.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	x, 332 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Power resourcesForecasting
Online resource:	https://doi.org/10.1007/978-3-031-94862-6
ISBN:	9783031948626$q(electronic bk.)

Machine learning technologies on energy economics and financeenergy and sustainable analytics.Volume 1 /
Machine learning technologies on energy economics and financeenergy and sustainable analytics.Volume 1 /[electronic resource] :edited by Mohammad Zoynul Abedin, Wang Yong. - Cham :Springer Nature Switzerland :2025. - x, 332 p. :ill. (some col.), digital ;24 cm. - International series in operations research & management science,v. 3672214-7934 ;. - International series in operations research & management science ;v. 140..

Analyzing Global Energy Patterns: Clustering Countries and Predicting Trends Towards Achieving Sustainable Development Goals -- Access to Energy Finance: Development of Renewable Energy in Bangladesh -- Explainable AI in Energy Forecasting: Understanding Natural Gas Consumption through Interpretable Machine Learning Models -- An Extensive Statistical Analysis of Time Series Modelling and Forecasting of Crude Oil Prices -- Comparative analysis of selected emerging economies energy transition scenario: A transition pathway for the continental neighbours -- Forecasting Energy Prices using Machine Learning Algorithms: A Comparative Analysis -- An Evidence-based Explainable AI Approach for Analyzing the Influence of CO2 Emissions on Sustainable Economic Growth -- BLDAR: A Blending Ensemble Learning Approach for Primary Energy Consumption Analysis -- Analyzing Biogas Production in Livestock Farms Using Explainable Machine Learning -- Application of Machine Learning Techniques in the Analysis of Sustainable Energy Finance -- Machine Learning and Deep Learning Strategies for Sustainable Renewable Energy: A Comprehensive Review -- Efficient Gasoline Spot Price Prediction using Hyperparameter Optimization and Ensemble Machine Learning Approach -- The Implications of Energy Transition and Development of Renewable Energy on Sustainable Development Goals of Two Asian Tigers.

This book explores the latest innovations in energy economics and finance, with a particular focus on the role of machine learning algorithms in advancing the energy sector. It examines key factors shaping this field, including market structures, regulatory frameworks, environmental impacts, and the dynamics of the global energy market. It discusses the critical application of machine learning (ML) in energy financing, introducing predictive tools for forecasting energy prices across various sectors-such as crude oil, electricity, fuelwood, solar, and natural gas. It also addresses how ML can predict investor behavior and assess the efficiency of energy markets, with a focus on both the opportunities and challenges in renewable energy and energy finance. This book serves as a comprehensive guide for academics, practitioners, financial managers, stakeholders, government officials, and policymakers who seek strategies to enhance energy systems, reduce costs and uncertainties, and optimize revenue for economic growth. This is the first volume of a two-volume set.

ISBN: 9783031948626$q(electronic bk.)

Standard No.: 10.1007/978-3-031-94862-6doiSubjects--Topical Terms:

999148
Power resources
--Forecasting

LC Class. No.: HD9502.A2

Dewey Class. No.: 333.79

Machine learning technologies on energy economics and financeenergy and sustainable analytics.Volume 1 /
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520 $a This book explores the latest innovations in energy economics and finance, with a particular focus on the role of machine learning algorithms in advancing the energy sector. It examines key factors shaping this field, including market structures, regulatory frameworks, environmental impacts, and the dynamics of the global energy market. It discusses the critical application of machine learning (ML) in energy financing, introducing predictive tools for forecasting energy prices across various sectors-such as crude oil, electricity, fuelwood, solar, and natural gas. It also addresses how ML can predict investor behavior and assess the efficiency of energy markets, with a focus on both the opportunities and challenges in renewable energy and energy finance. This book serves as a comprehensive guide for academics, practitioners, financial managers, stakeholders, government officials, and policymakers who seek strategies to enhance energy systems, reduce costs and uncertainties, and optimize revenue for economic growth. This is the first volume of a two-volume set.
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