Predicting Power Consumption Anomaly Using Statistical and Supervised Machine Learning Techniques


Tan, Yi Fei and Tan, Wooi Nee and El-Hadad, Rawan and Buhari, Abudhahir (2022) Predicting Power Consumption Anomaly Using Statistical and Supervised Machine Learning Techniques. In: 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC), 4-6 March 2022, Chongqing, China.

[img] Text
Predicting_Power_Consumption.pdf - Published Version
Restricted to Repository staff only

Download (1MB)


Power has become an essential element of daily life in the modern world. At the same time, over usage of electricity may lead to excessive power consumption, causing the device to short circuit or be on fire. Therefore, it is crucial to monitor and forecast the anomalies in power consumption to avoid any tragedy. In this paper, the authors proposed a method of predicting anomalies in power consumption. The proposed method uses a statistical approach in labeling; the labeled power consumption data are then used to form the data instances. Later, supervised machine learning classification techniques, namely Support Vector Machine, Decision Tree, and Random Forest, are implemented on the data instances to predict the power consumption anomalies. The experimental results demonstrate that the precision, recall, and F1 score are achieving better results when the training dataset is larger. Besides that, the results show that the techniques used to handle the imbalanced data will affect the performance of models.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Power consumption anomaly, predicting, statistical method, supervised machine learning, F1 score
Subjects: Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions: Faculty of Engineering (FOE)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 03 Nov 2022 01:54
Last Modified: 03 Nov 2022 01:54


Downloads per month over past year

View ItemEdit (login required)