Perspective
Open Access
Algorithmic Frenzy and the Reality Gap: The Dangerous Illusion of Grid Prediction Technology
1
College of Saint Petersburg Joint Engineering, Xuzhou University of Technology, Xuzhou 221018, China
Corresponding Author:
Yinzi Shao,
[email protected]
Volume 1, Issue 1
2025
Received: 01 September 2025
Accepted: 12 October 2025
Published: 16 March 2026
Article Information
Published in
Journal of Computational Optimization and Reasoning
Volume/Issue
Volume 1, Issue 1, 2025
Pages
13-15
Abstract
This Perspective discusses the limitations of current grid data prediction technologies and emphasizes the need for more reliable, interpretable, and data-driven approaches to support the increasing complexity of modern power systems and the ongoing energy transition.
Keywords
Grid Prediction Technology
Data Availability Statement
Not applicable.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
AI Use Statement
The authors declare that no generative AI was used in the preparation of this manuscript.
Ethical Approval and Consent to Participate
Not applicable.
References
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[CrossRef] [Google Scholar] - Ruan, J., Liang, G., Zhao, J., Zhao, H., Qiu, J., Wen, F., & Dong, Z. Y. (2023). Deep learning for cybersecurity in smart grids: Review and perspectives. Energy Conversion and Economics, 4(4), 233-251.
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Cite This Article
APA Style
Wang, Z., & Shao, Y. (2026). Algorithmic Frenzy and the Reality Gap: The Dangerous Illusion of Grid Prediction Technology. Journal of Computational Optimization and Reasoning, 1(1), 13–15. https://doi.org/10.62762/JCOR.2025.611300
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TY - JOUR AU - Wang, Zhouyu AU - Shao, Yinzi PY - 2026 DA - 2026/03/16 TI - Algorithmic Frenzy and the Reality Gap: The Dangerous Illusion of Grid Prediction Technology JO - Journal of Computational Optimization and Reasoning T2 - Journal of Computational Optimization and Reasoning JF - Journal of Computational Optimization and Reasoning VL - 1 IS - 1 SP - 13 EP - 15 DO - 10.62762/JCOR.2025.611300 UR - https://www.icck.org/article/abs/JCOR.2025.611300 KW - Grid Prediction Technology AB - This Perspective discusses the limitations of current grid data prediction technologies and emphasizes the need for more reliable, interpretable, and data-driven approaches to support the increasing complexity of modern power systems and the ongoing energy transition. SN - request pending PB - Institute of Central Computation and Knowledge LA - English ER -
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@article{Wang2026Algorithmi,
author = {Zhouyu Wang and Yinzi Shao},
title = {Algorithmic Frenzy and the Reality Gap: The Dangerous Illusion of Grid Prediction Technology},
journal = {Journal of Computational Optimization and Reasoning},
year = {2026},
volume = {1},
number = {1},
pages = {13-15},
doi = {10.62762/JCOR.2025.611300},
url = {https://www.icck.org/article/abs/JCOR.2025.611300},
abstract = {This Perspective discusses the limitations of current grid data prediction technologies and emphasizes the need for more reliable, interpretable, and data-driven approaches to support the increasing complexity of modern power systems and the ongoing energy transition.},
keywords = {Grid Prediction Technology},
issn = {request pending},
publisher = {Institute of Central Computation and Knowledge}
}
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Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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