東華大學圖書館 |

Machine learning technologies on energy economics and finance = energy and sustainable analytics.. Volume 2 /

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:	Green Driving: Harnessing Machine Learning to Predict Vehicle Carbon Footprints and Interpreting Results with Explainable AI -- A Comparative Evaluation of Deep Neural Networks for Electricity Price Forecasting -- Energy Forecasting Utilizing CNN-LSTM Attention Mechanism: Empirical Evidence from the Spanish Electricity Market -- Feature Selection and Explainable AI For Transparent Windmill Power Forecasting -- Improving the Analysis of CO2 Emissions with a Filter and Imputation-Based Processing Method -- A Study on the Efficacy of Machine Learning and Ensemble Learning in Wind Power Generation Analysis -- Predicting Solar Radiation: A Fusion Approach with CatBoost and Random Forest Ensemble Enhanced by Explainable AI -- Modeling Nuclear Fusion Reaction Occurrence with Advanced Deep Learning Techniques: Insights from LIME and SMOTE -- A Critical Study on LSTM AND TRANSFORMER Models for Financial Analysis and Forecasting -- Exploring Feature Selection Techniques in Predicting Indian Household Electricity Consumption -- Constructing Women Empowerment Indices-based on Kernel PCA and Evaluating Its Determinants: Evidence from BDHS -- An Ensemble Machine Learning Approach to Predicting CO2 Emission Rates: Evidence from Denmark's Energy Data Service -- Smart Grid Stability Analysis with Interpretable Machine Learning and Deep Learning Models -- Weather as a Critical Component in Investment Strategies: Insights for Stakeholders.
Contained By:	Springer Nature eBook
標題:	Power resources - Forecasting -
電子資源:	https://doi.org/10.1007/978-3-031-95099-5
ISBN:	9783031950995

Machine learning technologies on energy economics and finance = energy and sustainable analytics.. Volume 2 /
Machine learning technologies on energy economics and financeenergy and sustainable analytics.Volume 2 /[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. 3682214-7934 ;. - International series in operations research & management science ;v. 368..

Green Driving: Harnessing Machine Learning to Predict Vehicle Carbon Footprints and Interpreting Results with Explainable AI -- A Comparative Evaluation of Deep Neural Networks for Electricity Price Forecasting -- Energy Forecasting Utilizing CNN-LSTM Attention Mechanism: Empirical Evidence from the Spanish Electricity Market -- Feature Selection and Explainable AI For Transparent Windmill Power Forecasting -- Improving the Analysis of CO2 Emissions with a Filter and Imputation-Based Processing Method -- A Study on the Efficacy of Machine Learning and Ensemble Learning in Wind Power Generation Analysis -- Predicting Solar Radiation: A Fusion Approach with CatBoost and Random Forest Ensemble Enhanced by Explainable AI -- Modeling Nuclear Fusion Reaction Occurrence with Advanced Deep Learning Techniques: Insights from LIME and SMOTE -- A Critical Study on LSTM AND TRANSFORMER Models for Financial Analysis and Forecasting -- Exploring Feature Selection Techniques in Predicting Indian Household Electricity Consumption -- Constructing Women Empowerment Indices-based on Kernel PCA and Evaluating Its Determinants: Evidence from BDHS -- An Ensemble Machine Learning Approach to Predicting CO2 Emission Rates: Evidence from Denmark's Energy Data Service -- Smart Grid Stability Analysis with Interpretable Machine Learning and Deep Learning Models -- Weather as a Critical Component in Investment Strategies: Insights for Stakeholders.

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 second volume of a two-volume set.

ISBN: 9783031950995

Standard No.: 10.1007/978-3-031-95099-5doiSubjects--Topical Terms:

3787637
Power resources
--Forecasting

LC Class. No.: HD9502.A2

Dewey Class. No.: 333.79

Machine learning technologies on energy economics and finance = energy and sustainable analytics.. Volume 2 /
LDR:03688nmm a2200337 a 4500 001 2412727
003 DE-He213
005 20250807130442.0
006 m d
007 cr nn 008maaau
008 260204s2025 sz s 0 eng d
020 $a 9783031950995 $q (electronic bk.)
020 $a 9783031950988 $q (paper)
024 7 $a 10.1007/978-3-031-95099-5 $2 doi
035 $a 978-3-031-95099-5
040 $a GP $c GP
041 0 $a eng
050 4 $a HD9502.A2
072 7 $a KJMV $2 bicssc
072 7 $a BUS087000 $2 bisacsh
072 7 $a KJMV $2 thema
082 0 4 $a 333.79 $2 23
090 $a HD9502.A2 $b M149 2025
245 0 0 $a Machine learning technologies on energy economics and finance $h [electronic resource] : $b energy and sustainable analytics. $n Volume 2 / $c edited by Mohammad Zoynul Abedin, Wang Yong.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a x, 332 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a International series in operations research & management science, $x 2214-7934 ; $v v. 368
505 0 $a Green Driving: Harnessing Machine Learning to Predict Vehicle Carbon Footprints and Interpreting Results with Explainable AI -- A Comparative Evaluation of Deep Neural Networks for Electricity Price Forecasting -- Energy Forecasting Utilizing CNN-LSTM Attention Mechanism: Empirical Evidence from the Spanish Electricity Market -- Feature Selection and Explainable AI For Transparent Windmill Power Forecasting -- Improving the Analysis of CO2 Emissions with a Filter and Imputation-Based Processing Method -- A Study on the Efficacy of Machine Learning and Ensemble Learning in Wind Power Generation Analysis -- Predicting Solar Radiation: A Fusion Approach with CatBoost and Random Forest Ensemble Enhanced by Explainable AI -- Modeling Nuclear Fusion Reaction Occurrence with Advanced Deep Learning Techniques: Insights from LIME and SMOTE -- A Critical Study on LSTM AND TRANSFORMER Models for Financial Analysis and Forecasting -- Exploring Feature Selection Techniques in Predicting Indian Household Electricity Consumption -- Constructing Women Empowerment Indices-based on Kernel PCA and Evaluating Its Determinants: Evidence from BDHS -- An Ensemble Machine Learning Approach to Predicting CO2 Emission Rates: Evidence from Denmark's Energy Data Service -- Smart Grid Stability Analysis with Interpretable Machine Learning and Deep Learning Models -- Weather as a Critical Component in Investment Strategies: Insights for Stakeholders.
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 second volume of a two-volume set.
650 0 $a Power resources $x Forecasting $x Data processing. $3 3787637
650 0 $a Power resources $x Finance $x Data processing. $3 3787638
650 0 $a Energy industries $x Finance $x Data processing. $3 3787639
650 0 $a Machine learning. $3 533906
650 0 $a Artificial intelligence $x Financial applications. $3 3493836
650 1 4 $a Operations Management. $2 swd $3 1283589
650 2 4 $a IT in Business. $3 2114922
650 2 4 $a Financial Technology and Innovation. $3 3591723
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Energy Policy, Economics and Management. $3 1532761
650 2 4 $a Sustainability. $3 1029978
700 1 $a Abedin, Mohammad Zoynul. $3 3628520
700 1 $a Yong, Wang. $3 2139892
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a International series in operations research & management science ; $v v. 368. $3 3788302
856 4 0 $u https://doi.org/10.1007/978-3-031-95099-5
950 $a Business and Management (SpringerNature-41169)