Time Series Forecasting Analysis for Automated Smart Meter Reading System
Sujaudeen N.1, Lakshmi Priya2, Gayathri Venkatesan3, Dharshni M4
1Dr. Sujaudeen N., Assistant Professor, Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai (Tamil Nadu), India.
2Dr. Lakshmi Priya, Assistant Professor, Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai (Tamil Nadu), India.
3Gayathri Venkatesan, Student, Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai (Tamil Nadu), India.
4Dharshni M, Student, Department of Computer Science and Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai (Tamil Nadu), India.
Manuscript received on 25 April 2025 | First Revised Manuscript received on 30 April 2025 | Second Revised Manuscript received on 04 May 2025 | Manuscript Accepted on 15 May 2025 | Manuscript published on 30 May 2025 | PP: 13-20 | Volume-4 Issue-3, May 2025 | Retrieval Number: 100.1/ijeer.C104304030525 | DOI: 10.54105/ijeer.C1043.04030525
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© The Authors. Published by Lattice Science Publication (LSP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: Time Series Forecasting, SARIMA, SARIMAX, XGBoost, CATBoost, ARIMA, LSTM, Streaming Data, Real Time Data, Short Term Load Forecasting, Automatic Meter Reader(AMR), Smart Meter.
Scope of the Article: Renewable Energy