01113nam0 22002771i 450 SUN003288320050301120000.005-217-8580-420050225d1999 |0engc50 baengGB|||| |||||ˆThe ‰legal textsthe results of the Uruguay Round of multilateral trade negotiationsWorld trade organizationCambridgeCambridge University pressc1999IX, 492 p.23 cm.World Trade OrganizationFISUNC013790USNew YorkSUNL000011CambridgeSUNL000024World trade organizationSUNV008510265837Cambridge universitySUNV000097650ITSOL20181109RICASUN0032883UFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZA00 CONS XIII.Eed.5 00 17092 UFFICIO DI BIBLIOTECA DEL DIPARTIMENTO DI GIURISPRUDENZA17092CONS XIII.Eed.5paLegal texts1430716UNICAMPANIA04981nam 22007455 450 991102042900332120250807130442.03-031-95099-210.1007/978-3-031-95099-5(MiAaPQ)EBC32256164(Au-PeEL)EBL32256164(CKB)40138052700041(DE-He213)978-3-031-95099-5(EXLCZ)994013805270004120250807d2025 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierMachine Learning Technologies on Energy Economics and Finance Energy and Sustainable Analytics, Volume 2 /edited by Mohammad Zoynul Abedin, Wang Yong1st ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (435 pages)International Series in Operations Research & Management Science,2214-7934 ;3683-031-95098-4 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.International Series in Operations Research & Management Science,2214-7934 ;368Production managementBusiness information servicesFinancial engineeringMachine learningEnergy policyEnergy policySustainabilityOperations ManagementIT in BusinessFinancial Technology and InnovationMachine LearningEnergy Policy, Economics and ManagementSustainabilityProduction management.Business information services.Financial engineering.Machine learning.Energy policy.Energy policy.Sustainability.Operations Management.IT in Business.Financial Technology and Innovation.Machine Learning.Energy Policy, Economics and Management.Sustainability.658.5Abedin Mohammad Zoynul1837078Yong Wang1383645MiAaPQMiAaPQMiAaPQBOOK9911020429003321Machine Learning Technologies on Energy Economics and Finance4415428UNINA