The Intelligent Data Analysis Techniques and its Significant Impact on Managing Renewable Energy Resources

Ghada S. Mohammed; Osama Basil Ghazi; Ahmed Basil Ghazi; Salahuddin I. Abdrabba

doi:10.30526/37.4.3922

Authors

Ghada S. Mohammed Department of Computer Engineering Techniques, Madenat Alelem University College, Baghdad, Iraq. https://orcid.org/0000-0002-9367-3030
Osama Basil Ghazi Department of Computer Engineering Techniques, Madenat Alelem University College, Baghdad, Iraq. https://orcid.org/0000-0001-7095-4297
Ahmed Basil Ghazi Department of Computer, College of Education For Pure Science (Ibn Al-Haitham), Baghdad, Iraq. https://orcid.org/0000-0003-1761-6031
Salahuddin I. Abdrabba Department of Electrical and Electronic Engineering, University of Omar Al_Mukhtar, Al-Bayda, Libya. https://orcid.org/0000-0001-8485-6030

DOI:

https://doi.org/10.30526/37.4.3922

Keywords:

Renewable Energy, Intelligent Data Analysis, Deep Learning, Machine Learning, Green Energy

Abstract

Accelerated technological development has a significant role in highly increasing energy demand, which enhances the interest of companies and governments in adopting renewable energy sources and finding new technologies that enable the effective use of these sources, such as techniques for predicting the amounts of generated and consumed energy, technologies of energy storage, and others. The high complexity of energy networks also necessitates intelligent systems to manage energy production and distribution with high efficiency, which is based on intelligent data analysis techniques. This paper conducts a comprehensive analysis of the critical impact of intelligent data analysis techniques (Deep Learning and Machine Learning) in the management of renewable energy systems. Machine learning uses previous data to forecast and optimize energy production and consumption. While deep learning excels at dealing with complex connections and non-linear patterns, The analysis showed that using and developing these technologies in renewable energy applications improves decision-making processes by providing more accurate predictions, highly efficient resource usage, minimizing complexity in the computations and time of system implementation, and creating robust renewable energy systems with minimum cost. As the renewable energy sector grows, combining Deep Learning (DL) and Machine Learning (ML) will be critical to encouraging efficient investment and intelligent management practices in renewable energy and sustainability applications.

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