AI and Machine Learning Applications in Predicting Energy Market Prices and Trends

¹ Institute of Business Management, GLA University, Mathura,
² Department of Computer Engineering,Vishwakarma Institute of Technology Pune India manikrao.dhore@vit.edu
³ Asst Professor,Department of IT,New Prince Shri Bhavani College of Engineering and Technology Chennai - 600073, Tamil nadu,India,jansirani.d@newprinceshribhavani.com
⁴ Assistant Professor, School of Business and Management, CHRIST University, Bangaluru Email - sbjeshurun@gmail.com
⁵ Assistant Professor,Department of MECH,Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127.,g.Sathi_mech@psvpec.in
⁶ Professor and Head, Mechanical Engineering, Dr. D. Y. Patil Institute of Technology, Pimpri, Pune.
⁷ Associate Professor Department of Computer science and Engineering , AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India - 626005 vivekmsucse@gmail.com

^* gunjan.sharma@gla.ac.in

Abstract

The worldwide energy market is intricate and unstable, shaped by several aspects including geopolitical occurrences, supply-demand variations, and regulatory modifications. Precisely forecasting energy prices and trends is essential for stakeholders, such as energy producers, dealers, and policymakers. This study investigates the utilization of artificial intelligence (AI) and machine learning (ML) to improve energy price forecasting models. Conventional forecasting methods frequently fail to account for the dynamic and non-linear characteristics of energy markets; however, AI/ML techniques, including neural networks, decision trees, and reinforcement learning, provide enhanced prediction precision. By including external variables such as meteorological conditions and economic metrics, AI models can produce more accurate and useful insights. Case studies illustrate the effective implementation of AI in energy markets, showcasing its capacity to surpass traditional methods. This article addresses difficulties such as data quality and computing expenses while delineating potential developments in AI-driven energy market forecasts.