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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.
List of contents
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.
About the author
Dr. Mohammad Zoynul Abedin is a Senior Lecturer in FinTech, at the School of Management, Swansea University, UK. He received his B.B.A. and M.B.A. degrees in finance from the University of Chittagong, Bangladesh, and his D.Phil. degree in investment theory from the Dalian University of Technology, China. His work appears on the Annals of Operations Research, International Journal of Production Research, IEEE Transactions on Industrial Informatics, to mention a few. His current research interests include business data analytics, fintech, and computational finance. He is a fellow of the Financial Management Association (FMA), and British Accounting and Finance Association (BAFA).
Dr. Wang Yong is a Professor and the Vice Dean at the School of Statistics, Dongbei University of Finance and Economics (DUFE), China. Dr. Yong was awarded the Outstanding Professor by DUFE, and is also the chief expert of major projects at the National Social Science Foundation of China. His main research interests are energy economics, environmental-economic system analysis, and policy optimization. He has published more than 70 papers in reputable academic journals, including Annals of Operations Research, Technological Forecasting & Social Change, Journal of Environmental Management, Renewable and Sustainable Energy Reviews, and other prestigious journals.
Summary
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.
