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Power theft detection using random forest algorithm and compared with K-Nn algorithm.
- Source :
-
AIP Conference Proceedings . 2024, Vol. 2853 Issue 1, p1-7. 7p. - Publication Year :
- 2024
-
Abstract
- The aim of the study is to diminish the power loss with reference to sensitivity and precision in power transmissions and consumptions. As power theft is considered as non-technical loss, it is hard to track the total transmission. So the Random forest algorithm is proposed over the K-NN Algorithm to compare accuracy in power theft detection. Materials and Methods: There are two groups in this study each with a sample size of 21400 per group. Analysis is done with the pretest power 0.8. Results: The mean value for accuracy in the Random Forest algorithm is 90.6215 which is high when compared to K-NN algorithm whose accuracy is 82.2200. It has an insignificant value of 0.07 (p>0.05). These values are evaluated using SPSS for statistical analysis. Conclusion: This analysis shows that the novel Random Forest algorithm has more reliable accuracy and sensitivity of power theft detection compared to the K-NN algorithm. [ABSTRACT FROM AUTHOR]
- Subjects :
- *RANDOM forest algorithms
*THEFT
*ALGORITHMS
*POWER transmission
*STATISTICS
Subjects
Details
- Language :
- English
- ISSN :
- 0094243X
- Volume :
- 2853
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- AIP Conference Proceedings
- Publication Type :
- Conference
- Accession number :
- 177080427
- Full Text :
- https://doi.org/10.1063/5.0198770