ENERGY DEMAND PREDICTION AND THEFT DETECTION USING MACHINE LEARNING

Electrical Engineering

The project's main focus is on the thorough management of energy resources using demand forecasting, theft detection, and real-time monitoring. It uses voltage and AC current sensors to track energy usage; the collected data is timestamped and stored in a dataset. To overcome this difficulty, the use of Machine Learning (ML) methods into energy management systems has shown promise. Long Short-Term Memory (LSTM) neural networks are used to estimate the future energy consumption, which improves resource planning. Furthermore, a Support Vector Machine (SVM) algorithm trained on historical data is used to detect theft. The integration of these technologies is expected to transform the energy industry by promoting energy conservation, enabling proactive decision-making, and reducing financial losses brought on by energy theft. The collected data is safely uploaded to the ThingSpeak cloud platform, guaranteeing data accessibility and integrity. A web application is created to visualize energy usage, demand projections, and theft alarms in order to improve user involvement and control. In addition to addressing the urgent need for effective energy management, this initiative will help users fight energy theft, make educated decisions, and promote sustainability and resource conservation.

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