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Edge AI and Blockchain Integration: A Review of Renewable Energy Optimization
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Author(s): Slimane Hadjab (Faculty of Sciences Exacts and Computer Science, Mohammed Seddik Ben Yahia-Jijel University, Algeria), Amol D. Vibhute (Symbiosis Institute of Computer Studies and Research, Pune, India), Imane Aitouhanni (ENSIAS, SSLAB, Mohammed V University in Rabat, Morocco), Fatima Zahra Aoujil (Biotechnology, Materials, and Environment Team, Faculty of Sciences Agadir, Ibn Zohr University, Morocco), Marouane Marhoun (National School of Applied Science, Abdelmalek Essaadi University, Tetouan, Morocco), Brahim El Ouardi (Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco), Serkan Derici (Nevşehir Hacı Bektaş Veli University, Turkey), Yasmine El-Bakkouri (Research Laboratory in Management Sciences of Organizations, National School of Business and Management, Ibn Tofail University, Kenitra, Morocco), Yassine Mouniane (Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco)and Mohamed El Bakkali (Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco)
Copyright: 2026
Pages: 22
Source title:
Empowering Resilient Communities Through Climate Action, Renewable Energy, and Waste Management
Source Author(s)/Editor(s): Seda H. Bostancı (Tekirdağ Namık Kemal University, Turkey), Seda Yıldırım (Tekirdağ Namık Kemal University, Turkey)and Valentin Vasilev (Higher School of Security an Economics, Bulgaria)
DOI: 10.4018/979-8-3373-0862-3.ch009
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Abstract
In this paper, the impact of AI in energy analysis and management are elaborated along with providing an intelligent approach to make better the functionality of AI at renewables keeping along with edge intelligence integrated to blockchain for smart implementation as required. Moreover, the research gives an elaborated study on blockchain technology and edge intelligence areas along with advanced smart fields. The research further outlines on several AI techniques, such as machine learning and federated learning. This study utilizes a specialist literature review, examining recently published scientific publications and peer-reviewed literature from the last couple of years. Abstract The key results show significant progress in AI techniques for predicting and assessing RES to improve their performance. AI has proven to be very effective in accurately predicting the trends of energy consumption, allowing them to improve energy management strategies. This paper provides guidance to academia and practitioners on deploying AI for more sustainable and efficient energy-related analytics.
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