Machine Learning Technologies on Energy Economics and Finance by Abedin, Mohammad Zoynul; Yong, Wang;

Long description:

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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.

Table of Contents:

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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.