Power theft detection using random forest algorithm and compared with K-Nn algorithm - Reddy, K. Bhupal; Malathi, K.; Priya, M. Vishnu (AIP conference proceedings, 2024)
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.