Machine Learning Based Decision Trees for Energy Meter Inspection in Power Sector
| International Journal of Electrical and Electronics Engineering |
| © 2024 by SSRG - IJEEE Journal |
| Volume 11 Issue 6 |
| Year of Publication : 2024 |
| Authors : Ashpana Shiralkar, Haripriya Kulkarni, Poonam Mane, Shashikant Bakre |
10.14445/23488379/IJEEE-V11I6P114
How to Cite?
Ashpana Shiralkar, Haripriya Kulkarni, Poonam Mane, Shashikant Bakre, "Machine Learning Based Decision Trees for Energy Meter Inspection in Power Sector," SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 6, pp. 130-135, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I6P114
Abstract:
This research paper presents an innovative approach to energy meter inspection within the power sector, leveraging the power of machine learning and decision tree analysis. The study seeks to enhance the accuracy and efficiency of inspections by employing a data-driven methodology. By utilizing decision trees, the model can effectively classify and identify meter anomalies, potential defects, and performance irregularities. The integration of machine learning enables the system to adapt and improve over time, ensuring precise and consistent inspections. The results indicate a significant improvement in inspection outcomes, reducing human error and enhancing the overall quality control process. This approach holds promise for more reliable and efficient energy meter inspections in the power sector, ultimately contributing to improved service quality and energy accountability. The novice Machine Learning approach method based on Decision trees and Random Forests is proposed based on a case study of one of the meter manufacturers in India.
Keywords:
Decision Trees, Machine Learning, Random Forests, Energy meters, Inspection optimization.
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