Time Series Forecasting Analysis for Automated Smart Meter Reading System
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Abstract
The Smart Meter Reading System modernizes traditional meter reading by enabling real-time energy monitoring and short-term consumption predictions for sectors like banking and automotive. The research in this paper focuses on the comparative application of time series forecasting techniques for enhancing the performance of Automated Smart Meter Reading(AMR) Systems. With a growing need for efficient energy management, especially in the context of smart grids and real-time analysis, this study explores how advanced machine learning and deep learning models can predict electricity energy consumption based on smart meter data. The research uses a realworld dataset from the UCI Machine Learning Repository: https://archive.ics.uci.edu/dataset/290/tamilnadu+electricity+boar d+hourly+readings. The study leverages time series forecasting models, including ARIMA, SARIMA, SARIMAX, LSTM, XGBoost, and CATBoost, to capture trends, seasonality, and long-term dependencies in sequential data. Each model is evaluated on benchmark metrics such as Root Mean Squared Error( RMSE) and Mean Squared Error (MSE) to measure forecasting accuracy. We have observed that the best models for our purpose of short-term predictions are our two ensemble models. We also find XGBoost to have significantly high predictive reliability. Traditional models like ARIMA have not produced adequate results for the energy data. This study is significant as it demonstrates that integrating machine learning and deep learning into AMR systems enhances the intelligence and responsiveness of the overall system. Accurate forecasting allows utilities to make informed decisions, optimize grid load, and foster consumer awareness. The findings advocate for adopting advanced data-driven methods in modern energy infrastructures to promote efficiency, reliability, and sustainability.
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