Version 1
: Received: 26 June 2023 / Approved: 27 June 2023 / Online: 27 June 2023 (09:37:59 CEST)
How to cite:
Fasogbon, S.K.; Shaibu, S.A. Energy Grid Optimization Using Deep Machine Learning: A Review of Challenges and Opportunities. Preprints2023, 2023061874. https://doi.org/10.20944/preprints202306.1874.v1
Fasogbon, S.K.; Shaibu, S.A. Energy Grid Optimization Using Deep Machine Learning: A Review of Challenges and Opportunities. Preprints 2023, 2023061874. https://doi.org/10.20944/preprints202306.1874.v1
Fasogbon, S.K.; Shaibu, S.A. Energy Grid Optimization Using Deep Machine Learning: A Review of Challenges and Opportunities. Preprints2023, 2023061874. https://doi.org/10.20944/preprints202306.1874.v1
APA Style
Fasogbon, S.K., & Shaibu, S.A. (2023). Energy Grid Optimization Using Deep Machine Learning: A Review of Challenges and Opportunities. Preprints. https://doi.org/10.20944/preprints202306.1874.v1
Chicago/Turabian Style
Fasogbon, S.K. and Samuel Adavize Shaibu. 2023 "Energy Grid Optimization Using Deep Machine Learning: A Review of Challenges and Opportunities" Preprints. https://doi.org/10.20944/preprints202306.1874.v1
Abstract
The optimization of the energy grid is a critical task for ensuring a sustainable and efficient energy future. Deep machine learning techniques have the potential to improve energy grid optimization by predicting energy demands and supplies, optimizing energy production and distribution, and detecting and preventing fraud. However, there are also several challenges associated with the use of deep machine learning in energy grid optimization. These include the lack of standardized datasets and data quality issues, interpretability and explain-ability of machine learning models, ethical and social implications of using machine learning, and integration with existing energy infrastructure and regulatory frameworks. Having a clear understanding that continued research and developments in deep learning applications to energy field are crucial for achieving a sustainable and efficient energy future. This paper therefore reviewed existing literature for challenges and opportunities associated with deep machine learning in energy grid optimization; and highlights the importance of continued research and development in the field. The paper found out that opportunities for future research in deep machine learning for energy grid optimization include advancements in machine learning algorithms and techniques, development of new datasets and data collection methods, integration of machine learning with other emerging technologies. It also established needs for collaborative research and public-private partnerships.
Keywords
deep machine learning; sustainable energy; energy grid optimization; regulatory frameworks; interpretability
Subject
Engineering, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
