Machine Learning Technologies on Energy Economics and Finance

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.