View Proposal
-
Proposer
-
Medhdhar Salem Ali Muthanna Al- Gaashani
-
Title
-
Data-Driven Electricity Theft Detection Using Deep Learning on Smart Meter Data
-
Goal
-
Build a machine-learning system that detects electricity theft (non-technical losses) from smart-meter consumption data, classifying users as normal or fraudulent to help utilities reduce financial losses and improve grid safety
-
Description
- Electricity theft causes significant financial losses, while manual inspection is costly and inefficient. This project develops a data-driven detector using historical smart-meter consumption data. Students preprocess and normalize the data, extract useful consumption features, handle class imbalance, and compare a traditional model such as Random Forest or SVM with a deep-learning model such as a 1D CNN or LSTM. Performance is evaluated using precision, recall, F1-score, AUC, and confusion matrices. Optionally, SHAP can be used to explain suspicious consumption patterns, and a simple dashboard can display flagged.
- Resources
-
Chen, J., Nanehkaran, Y. A., Chen, W., Liu, Y., & Zhang, D. (2023). Data-driven intelligent method for detection of electricity theft. International Journal of Electrical Power & Energy Systems, 148, 108948 — https://doi.org/10.1016/j.ijepes.2023.108948 ; Paper code and data: https://github.com/xtu502/electricity-theft-detection ; SGCC real-world benchmark dataset
-
Background
-
-
Url
-
-
Difficulty Level
-
Moderate
-
Ethical Approval
-
None
-
Number Of Students
-
1
-
Supervisor
-
Medhdhar Salem Ali Muthanna Al- Gaashani
-
Keywords
-
deep learning, machine learning, smart grid, time-series, anomaly detection
-
Degrees
-
Bachelor of Science in Computational Sciences and Software Engineering