A COMPREHENSIVE TIME SERIES ANALYSIS OF ANNUAL FOSSIL-BASED ELECTRICAL ENERGY PRODUCTION ACROSS KEY NATIONS (1985-2022)

Abstract

This study addresses the critical challenge of accurately predicting annual electrical energy production derived from fossil sources across diverse nations, including Türkiye, Germany, England, France, Iran, and Ukraine spanning the years 1985 to 2022. Employing a combination of advanced deep learning techniques and traditional statistical models, the research aims to enhance forecasting precision and contribute to a deeper understanding of global energy consumption dynamics. By leveraging time series analysis, the study captures the intricate temporal patterns inherent in fossilbased energy production data, enabling robust predictions over an extended historical timeframe. Performance evaluation metrics such as Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) are utilized to rigorously assess the accuracy and reliability of the developed forecasting models. The scientific significance of this study lies in its ability to provide valuable insights into the complex interplay between socio-economic factors, technological advancements, and environmental considerations shaping fossil-based energy production trends. By conducting a comparative analysis across multiple countries with distinct energy landscapes, the research not only elucidates regional variations but also underscores the need for tailored forecasting approaches to address unique contextual challenges. The findings of this study hold significant implications for energy policymakers, industry stakeholders, and researchers striving to optimize resource allocation, mitigate environmental impacts, and transition towards sustainable energy systems. By advancing the state-of-the-art in energy forecasting methodologies, this research contributes to the broader goal of achieving a more resilient, equitable, and environmentally sustainable energy future.