IoT-Enhanced Machine Learning for Intelligent Energy Optimization and Predictive Management

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 11

Year of Publication : 2024

Authors : D. Rajalakshmi, K. Sudharson, R. Akhil Nair, M.A. Starlin

10.14445/23488549/IJECE-V11I11P115

How to Cite?

D. Rajalakshmi, K. Sudharson, R. Akhil Nair, M.A. Starlin, "IoT-Enhanced Machine Learning for Intelligent Energy Optimization and Predictive Management," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 11, pp. 168-178, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I11P115

Abstract:

IoT and machine learning systems are changing the energy management landscape since they make it possible to understand and analyze data with great detail. In this work, we develop EnerSense, a novel architecture that integrates IoT functionalities for smart meter data extraction with state-of-the-art Machine Learning techniques to manage energy consumption and project energy load. This model is based on the hybrid model, Random Forest and AutoRegressive Integrated Moving Average (RF-ARIMA), and it has an accuracy of 96% in determining the consumption behavior and investigating the outliers. Our framework enables wireless IoT integration and real-time data tracking for effective energy management while reducing cost regimes. Substantial empirical tests show about 20% energy wastage reduction, proving that the system can further improve energy efficiency. This solution enables utility companies to be equipped with meaningful energy usage strategies, presenting a cost-effective structure that optimizes resource use by meeting energy needs promptly and enhancing smart energy systems.

Keywords:

Smart meters, Wireless IoT, Machine Learning, Energy utilization optimization, Anomaly detection.

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