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Machine learning technologies on ene...

Abedin, Mohammad Zoynul.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning technologies on energy economics and finance/ edited by Mohammad Zoynul Abedin, Wang Yong.
其他題名:	energy and sustainable analytics.
其他作者:	Abedin, Mohammad Zoynul.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	x, 332 p. :ill. (some col.), digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Power resources - Forecasting -
電子資源:	https://doi.org/10.1007/978-3-031-94862-6
ISBN:	9783031948626

Machine learning technologies on energy economics and finance = energy 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 ;volume 367..

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

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

3787637
Power resources
--Forecasting

LC Class. No.: HD9502.A2

Dewey Class. No.: 333.79

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