Smart Meter-Based Power Consumption Detection System: An Integrated Approach Using LSTM Networks and Real-Time Web Interface

Author(s)	Chinmay Sarvansh Sinha
Country	India
Abstract	This research presents a comprehensive system for detecting and predicting power consumption using smart meter data. The proposed system integrates machine learning models, specifically Long Short-Term Memory (LSTM) networks, with a real-time web interface to analyze high-frequency electricity usage data. By leveraging smart meter data collected at three-minute intervals, the system identifies consumption patterns, predicts future usage, and detects anomalies in real time. The implementation uses Python-based tools such as TensorFlow for model development and Flask for web deployment. The system demonstrates significant improvements in prediction accuracy compared to traditional methods, achieving a mean absolute error (MAE) of 0.05 kWh and reducing computational time by 85% through parallel processing. This paper discusses the methodology, implementation, results, and potential applications of this system in energy management.
Keywords	Smart Meters,Power Consumption Prediction,Long Short-Term Memory (LSTM) Networks,Real-Time Energy Management,Machine Learning in Utilities,Demand Response Optimization,Flask Web Interface,Parallel Processing in Energy Forecasting
Field	Physics > Energy
Published In	Volume 7, Issue 1, January-February 2025
Published On	2025-02-28
DOI	https://doi.org/10.36948/ijfmr.2025.v07i01.37948

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International Journal For Multidisciplinary Research