AI-Driven Energy Trading Platforms: Market Dynamics and Challenges

^* College of technical engineering, The Islamic university, Najaf
^† Department of ECE, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai - 127
^‡ Tashkent State Pedagogical University, Tashkent, Uzbekistan
^§ Department of Management, Uttaranchal Institute of Management, Uttaranchal University, Dehradun 248007, India
^** New Prince Shri Bhavani college of Engineering and Technology, Anna University .
⁶ Associate Professor, AAA College of Engineering and Technology, Virdhunagar India .
⁷ Associate Professor, Dr. D. Y. Patil Institute of Technology, Pimpri

^* Corresponding Author:Iraq.ahmedhussienradie@iunajaf.edu.iq,
^† j.jaisudha_ece@psvpec.in
^‡ naziraabduraimovna1973@gmail.com
^§ neeti.cm@gmail.com
^** hodece@newprinceshribhavani.com

Abstract

The rapid evolution of the energy sector is significantly influenced by the integration of Artificial Intelligence (AI) technologies. This paper reviews the work in the areas of AI applications in energy trading platforms, focusing on three broad domains. Firstly, the energy industry is undergoing a transformative phase, where AI-driven digitalization is optimizing energy supply, trade, and consumption. Emphasis is laid on AI’s role in integrating solar and hydrogen power generation, supply-demand management, and the latest advancements in AI technology. These techniques have shown superior performance in areas like big data handling, cyberattack prevention, and energy efficiency optimization. Secondly, the manufacturing sector is witnessing a shift towards smart factories, where AI is enhancing value-added manufacturing by integrating various information communication technologies. The characteristics of these factories include operations optimization and intelligent decision-making, with AI technologies enabling systems to adapt to external needs. Lastly, while AI promises transformative changes in the energy sector, it also brings forth challenges. A multidisciplinary approach identifies these challenges, offering insights and recommendations for successful AI integration in the energy sector.