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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) International Journal of Applied Power Engineering (IJAPE)
Maguluri, Lakshmana Phaneendra
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Control & Systems Engineering Electrical & Electronics Engineering

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A comprehensive evaluation of machine learning algorithms for precise energy consumption forecasting in smart homes Maguluri, Lakshmana Phaneendra; Shankar, M.; Aruna, R.; Devi, D. Chitra; Suganya, M. J.
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i4.pp2138-2144

Abstract

Energy is one of the most critical and costly resources, playing a vital role in our daily lives. As technology advances, the demand for energy also increases. This work proposes a model for predicting energy consumption in smart homes, consisting of data preprocessing, performance evaluation, and application. Once the data is processed, it is fed into the prediction module, where various machine-learning algorithms are applied to forecast energy consumption. As smart home environments grow in complexity, selecting the most effective machine learning algorithm becomes increasingly crucial. The persistent challenge lies in manually discerning the best-performing algorithm, given their potential variance in efficacy across diverse use cases or datasets. In the dynamic landscape of energy conservation and cost-effective power generation, precise forecasting of energy consumption is essential, playing a pivotal role in advancing energy sustainability and bolstering economic stability. This introduction explores the intricate terrain of predicting energy utilization within smart homes, a domain that has seen increased interest due to the integration of machine learning algorithms. The primary focus of this exploration is the rigorous evaluation of these algorithms, using key performance metrics such as mean absolute error (MAE), mean squared error (MSE), and R-squared.
Design and implementation of solar-grid based charging station for electric vehicle with fault detection method using R-Pi and IoT processor Vaigundamoorthi, M.; Karthick, S.; Chandrika, V. S.; Chithra, D.; Balaramakrishna, K. V.; Khandan, K. Lakshmi; Maguluri, Lakshmana Phaneendra; Chandrasekar, S.; Janarthanan, M.
International Journal of Applied Power Engineering (IJAPE) Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v14.i4.pp794-802

Abstract

In this research describes the electrical vehicle (EV) charging station using PV panel with fault detection methods. The PV modules will failure for some time, because of some external factors and internal factors. In direct fault condition the monitor and analyze the external factors such as the life span, high intensity and breakage of the PV panels using Raspberry Pi (R-Pi) processor with internet of things (IoT) system. In power demand/day on the PV panel will be evaluated and analyzed through R-Pi processor and IoT. The efficiency and the range values of the PV panels will be monitored and analyzed through IoT. Proposed work explains, how the fault detection techniques have been improved and adopted in using R-Pi processor through IoT platform. The proposed dataset pre-processing system is incorporated with IoT module. The grid fault clearing time will be compared with the actual values through R-Pi processor. The PV panel faults are detected using thermal image processing, that image parameter values analysis through IoT based internal monitoring system.
Co-Authors Aruna, R. Balaramakrishna, K. V. Chandrasekar, S. Chandrika, V. S. Chithra, D. Devi, D. Chitra Janarthanan, M. Karthick, S. Khandan, K. Lakshmi Shankar, M. Suganya, M. J. Vaigundamoorthi, M.