Machine Learning Technologies on Energy Economics and Finance

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.

• Analyzing Global Energy Patterns: Clustering Countries and Predicting Trends Towards Achieving Sustainable Development Goals.- Access to Energy Finance: Development of Renewable Energy in Bangladesh.- Explainable AI in Energy Forecasting: Understanding Natural Gas Consumption through Interpretable Machine Learning Models.- An Extensive Statistical Analysis of Time Series Modelling and Forecasting of Crude Oil Prices.- Comparative analysis of selected emerging economies energy transition scenario: A transition pathway for the continental neighbours.- Forecasting Energy Prices using Machine Learning Algorithms: A Comparative Analysis.- An Evidence-based Explainable AI Approach for Analyzing the Influence of CO2 Emissions on Sustainable Economic Growth.- BLDAR: A Blending Ensemble Learning Approach for Primary Energy Consumption Analysis.- Analyzing Biogas Production in Livestock Farms Using Explainable Machine Learning.- Application of Machine Learning Techniques in the Analysis of Sustainable Energy Finance.- Machine Learning and Deep Learning Strategies for Sustainable Renewable Energy: A Comprehensive Review.- Efficient Gasoline Spot Price Prediction using Hyperparameter Optimization and Ensemble Machine Learning Approach.- The Implications of Energy Transition and Development of Renewable Energy on Sustainable Development Goals of Two Asian Tigers.