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Harnessing the Power of Artificial Intelligence for Collaborative Energy Optimization Platforms.

Authors :
Stecyk, Adam
Miciuła, Ireneusz
Source :
Energies (19961073). Jul2023, Vol. 16 Issue 13, p5210. 20p.
Publication Year :
2023

Abstract

This scientific paper highlights the critical significance of energy in driving sustainable development and explores the transformative potential of Artificial Intelligence (AI) tools in shaping the future of energy systems. As the world faces mounting challenges in meeting growing energy demands while minimizing environmental impact, there is a pressing need for innovative solutions that can optimize energy generation, distribution, and consumption. AI tools, with their ability to analyse vast amounts of data and make intelligent decisions, have emerged as a promising avenue for advancing energy systems towards greater efficiency, reliability, and sustainability. This paper underscores the importance of energy in sustainable development and investigates how AI tools can catalyse the next phase of human civilization. This paper presents a comprehensive review of the Collaborative Energy Optimization Platform (CEOP), an innovative model that utilizes AI algorithms in an integrated manner. The review of the CEOP model is based on an in-depth analysis of existing literature, research papers, and industry reports. The methodology encompasses a systematic review of the model's key features, including collaboration, data-sharing, and AI algorithm integration. The conducted research demonstrates the effectiveness of applying MCDM methods, specifically fuzzy AHP and TOPSIS, in evaluating and ranking the performance of five Collaborative Energy Optimization Platforms (CEOP models) across 20 sub-criteria. The findings emphasize the need for a comprehensive and holistic approach in assessing AI-based energy optimization systems. The research provides valuable insights for decision-makers and researchers in the field, fostering the development and implementation of more efficient and sustainable AI-powered energy systems. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19961073
Volume :
16
Issue :
13
Database :
Academic Search Index
Journal :
Energies (19961073)
Publication Type :
Academic Journal
Accession number :
164922092
Full Text :
https://doi.org/10.3390/en16135210