| Issue |
E3S Web Conf.
Volume 581, 2024
Empowering Tomorrow: Clean Energy, Climate Action, and Responsible Production
|
|
|---|---|---|
| Article Number | 01006 | |
| Number of page(s) | 9 | |
| DOI | https://doi.org/10.1051/e3sconf/202458101006 | |
| Published online | 21 October 2024 | |
Predictive Modeling of Energy Consumption in Smart Grids using Artificial Neural Networks
1 Moscow State University of Civil Engineering,129337, Yaroslavskoe shosse, 26, Moscow, Russia
2 Uttaranchal University, Dehradun- 248007, India
3 Department of MBA, KG Reddy College of Engineering and Technology, Chilkur(Vil), Moinabad(M), Ranga Reddy(Dist), Hyderabad, 500075,Telangana, India.
4 Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India
5 Department of computers Techniques engineering, College of technical engineering, The Islamic University, Najaf, Iraq
6 Lovely Professional University, Phagwara, Punjab, India
7 Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh-174103 India
8 Department of Electronics & Communication Engineering, GLA University, Mathura - 281406 (U.P.), India
9 Department of CSE, GRIET, Bachupally, Hyderabad, Telangana, India.
* Corresponding Author: ravanthi1743@grietcollege.com
This study delves into the ground-breaking applications of optical fiber grids for material analysis. In it, we look at the correlation between light intensity and temperature, analyze the material composition, and conduct a comprehensive examination into sensor calibration. Optical fiber grids are quite accurate in detecting changes in temperature and refractive index, as shown by the calibration results, which showed an outstanding average accuracy of 98%. The grids were able to distinguish between different materials with an average accuracy of 96%, according to the material composition research. The correct identification of a polymer sample with 45% polyethylene and 55% polypropylene demonstrated this. Also, the grids were able to properly react to changing temperatures since there was a strong linear relationship between light intensity and temperature (92 percent explanatory power). Taken together, the findings highlight optical fiber grids’ versatility and reliability, showing how they might revolutionize material research across several industries.
Key words: Optical fiber grids / smart grid / Calibration / Material composition / Temperature-light intensity correlation
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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