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Real-time implementation of an IoT-based intelligent source allocation system using machine learning technique in a commercial building

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Abstract

Building Energy Management is one of the prominent means of devising building-specific energy strategy and most of the research studies address the approach from the demand end. While, this study presents supply side management toward the challenge of implementing supply side management, due to the uncertainty of renewable power sources which are effectively tackled in the real-time scenario. In this paper, a novel Internet of Things-based Intelligent Source Allocation System (IoT-ISAS) has been designed specifically for the implementation within commercial buildings. The primary goal of this system is to ensure consistent and reliable power supply to critical loads within the building's direct current (DC) infrastructure. By leveraging a machine learning Decision Tree algorithm, the IoT-ISAS continuously allocates power sources to critical loads, thereby optimizing efficiency and minimizing downtime. The IoT-ISAS has been successfully deployed in a real-time commercial establishment at M/s Quantanics Techserv Private Limited. The IoT-ISAS incentivizes the commercial consumers to increase their utilization of renewable power sources and thereby, alleviates the burden on the grid. Based on the obtained results, it is observed that the sources are effectively shared and the tariff is found to be reduced by 9.76% for the best source allocating scenario using IoT-ISAS.

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Acknowledgements

The authors express their gratitude to the Quantanics Techserv Pvt. Ltd. Company, Madurai and Thiagarajar College of Engineering, Madurai for the financial support under the Thiagarajar Research Fellowship, File No. TCE/TRF-Jan2022/07 for carrying out this research work.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai, Tamil Nadu, India

    V. Suresh Kumar, S. Parameswari & S. Charles Raja

  2. Quantanics Techserv Pvt.Ltd, Madurai, Tamil Nadu, India

    T. Karthick

Authors
  1. V. Suresh Kumar
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  2. S. Parameswari
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  3. S. Charles Raja
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  4. T. Karthick
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Contributions

S. Parameswari contributed to conceptualization, methodology, software, writing—original draft; V. Suresh Kumar contributed to supervision, validation, writing—review & editing; S. Charles Raja contributed to data curation, investigation & visualization; T.Karthick contributed to formal analysis and software

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Correspondence to V. Suresh Kumar.

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Kumar, V.S., Parameswari, S., Raja, S.C. et al. Real-time implementation of an IoT-based intelligent source allocation system using machine learning technique in a commercial building. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02389-6

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  • DOI: https://doi.org/10.1007/s00202-024-02389-6

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