A Hybrid Machine Learning Approach for Electricity Theft Detection and Demand Prediction

Electricity theft and unpredicted energy demand pose major challenges to modern power systems, resulting in financial losses and supply inefficiencies. This project introduces a hybrid machine learning-based solution that combines real-time monitoring, theft detection, and demand prediction using cost-effective IoT components. Voltage and current data from two areas are collected using NodeMCU and MCP3008 modules and processed on a cloud platform. The acquired data is processed and analyzed in Google Colab, where a Support Vector Machine (SVM) model detects anomalies indicative of energy theft with high accuracy. Additionally, the project incorporates a demand prediction system based on a Long Short-Term Memory (LSTM) neural network. The LSTM model forecasts future energy demand with remarkable precision, allowing utilities to optimize power generation and distribution. An SVM model is employed for classifying anomalies indicative of electricity theft, while an LSTM network forecasts future energy demands. Accuracy is improved by incorporating time-series preprocessing, data normalization, and model tuning, while reliability is ensured through continuous sensor monitoring, stable data transmission, and relay control, enabling immediate response to detected theft or anomalies. These features reduce downtime, prevent data loss, and ensure the system operates consistently. The proposed system©s dual-layer architecture provides timely predictions and theft identification, making it suitable for smart grid applications. By leveraging both classification and forecasting models, the system ensures proactive energy management, minimizes losses, and enhances operational stability. By integrating anomaly detection with predictive analytics, this system enhances grid efficiency, reduces losses, and strengthens energy security.