Abstract
This study explores the application of artificial intelligence (AI) in forecasting energy prices by developing advanced models that integrate diverse data sources, including market data, weather patterns, and geopolitical events. Traditional forecasting methods, such as ARIMA and GARCH, have shown limitations in handling energy markets’ complex, non-linear relationships. In contrast, AI models demonstrate superior accuracy and adaptability, particularly deep learning and ensemble methods. The research findings indicate that AI-assisted models outperform traditional approaches, providing more reliable predictions and valuable insights for energy companies and policymakers. These models enhance risk management, optimize production schedules, and support strategic decision-making, ultimately contributing to more stable and efficient energy markets. The study also highlights the importance of integrating multiple data sources and suggests future research directions, including the development of real-time forecasting systems and explainable AI techniques. The implications of this research are significant, offering new tools for navigating the complexities of global energy markets and improving decision-making processes in both industry and policy.
Published Version
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