Power outage prediction by using logistic regression and decision tree
The occurrence of the power outage caused inconvenience to the customers including the energy suppliers. There are various factors that can trigger the power outage such as lightning, weather or animal. In this paper, the power outage prediction has been performed by using the datasets provided whic...
| Main Authors: | , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IOP Publishing
2021
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/32879/ http://umpir.ump.edu.my/id/eprint/32879/1/Power%20outage%20prediction%20by%20using%20logistic%20regression.pdf |
| _version_ | 1848824131870523392 |
|---|---|
| author | Alia Yasmin, Nor Saidi Nor Azuana, Ramli Noryanti, Muhammad Lilik Jamilatul, Awalin |
| author_facet | Alia Yasmin, Nor Saidi Nor Azuana, Ramli Noryanti, Muhammad Lilik Jamilatul, Awalin |
| author_sort | Alia Yasmin, Nor Saidi |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The occurrence of the power outage caused inconvenience to the customers including the energy suppliers. There are various factors that can trigger the power outage such as lightning, weather or animal. In this paper, the power outage prediction has been performed by using the datasets provided which are lightning data and tripping report. The machine learning method was carried out to predict the power outage occurrence by using the Classification Learner App in MATLAB. Before performing the machine learning method, the data went through the data pre-processing to ensure the data is clean and the significant feature for prediction can be selected to run in the Classification Learner App. The results of this research have shown that Fine Tree is the most suitable model to be used for the prediction of power outage. The results have been compared by using the Area Under Curve (AUC) in Receiving Operating Characteristic (ROC). Logistic Regression and Coarse Tree shows the lowest value of AUC compared to other model and Fine Tree has the highest value of AUC. |
| first_indexed | 2025-11-15T03:08:10Z |
| format | Conference or Workshop Item |
| id | ump-32879 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:08:10Z |
| publishDate | 2021 |
| publisher | IOP Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-328792022-02-04T03:52:54Z http://umpir.ump.edu.my/id/eprint/32879/ Power outage prediction by using logistic regression and decision tree Alia Yasmin, Nor Saidi Nor Azuana, Ramli Noryanti, Muhammad Lilik Jamilatul, Awalin TK Electrical engineering. Electronics Nuclear engineering The occurrence of the power outage caused inconvenience to the customers including the energy suppliers. There are various factors that can trigger the power outage such as lightning, weather or animal. In this paper, the power outage prediction has been performed by using the datasets provided which are lightning data and tripping report. The machine learning method was carried out to predict the power outage occurrence by using the Classification Learner App in MATLAB. Before performing the machine learning method, the data went through the data pre-processing to ensure the data is clean and the significant feature for prediction can be selected to run in the Classification Learner App. The results of this research have shown that Fine Tree is the most suitable model to be used for the prediction of power outage. The results have been compared by using the Area Under Curve (AUC) in Receiving Operating Characteristic (ROC). Logistic Regression and Coarse Tree shows the lowest value of AUC compared to other model and Fine Tree has the highest value of AUC. IOP Publishing 2021 Conference or Workshop Item PeerReviewed pdf en cc_by http://umpir.ump.edu.my/id/eprint/32879/1/Power%20outage%20prediction%20by%20using%20logistic%20regression.pdf Alia Yasmin, Nor Saidi and Nor Azuana, Ramli and Noryanti, Muhammad and Lilik Jamilatul, Awalin (2021) Power outage prediction by using logistic regression and decision tree. In: Journal of Physics: Conference Series; Simposium Kebangsaan Sains Matematik ke-28 (SKSM28) , 28 - 29 July 2021 , Kuantan, Pahang. pp. 1-10., 1988 (1). ISSN 1742-6596 (Published) https://doi.org/10.1088/1742-6596/1988/1/012039 https://doi.org/10.1088/1742-6596/1988/1/012039 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Alia Yasmin, Nor Saidi Nor Azuana, Ramli Noryanti, Muhammad Lilik Jamilatul, Awalin Power outage prediction by using logistic regression and decision tree |
| title | Power outage prediction by using logistic regression and decision tree |
| title_full | Power outage prediction by using logistic regression and decision tree |
| title_fullStr | Power outage prediction by using logistic regression and decision tree |
| title_full_unstemmed | Power outage prediction by using logistic regression and decision tree |
| title_short | Power outage prediction by using logistic regression and decision tree |
| title_sort | power outage prediction by using logistic regression and decision tree |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/32879/ http://umpir.ump.edu.my/id/eprint/32879/ http://umpir.ump.edu.my/id/eprint/32879/ http://umpir.ump.edu.my/id/eprint/32879/1/Power%20outage%20prediction%20by%20using%20logistic%20regression.pdf |