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An AI-driven fault-tolerant aggregation model for smart grid

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Abstract

Data aggregation is considered as the crucial aspect of the smart grid to assess the electricity consumption information which acts as a basis of management decision making for utility provider. However, smart meters are lightweight devices therefore, subject to technical crashes and deferment in forwarding their data that hinders the efficient and accurate communication of data. Consequently, the consumption data of the faulty smart meters remain uncommunicated which can affect the accuracy of the final aggregate significantly and can misguide the utility companies in the power management decisions. Therefore, considering the stated challenge, a novel AI-driven fault-tolerant aggregation model is proposed that is established on the prediction mechanism of artificial neural network. The novelty of the proposed work lies in the efficient fault-handling of unavailability of data by utilizing previous available information. Furthermore, the proposed model also provides mechanism to track the affected meters and acknowledge the utility provider. The experimental simulation and comparison results reveal the outperforming results of the proposed model from state-of-the-art works which is validated using root mean squared error and mean absolute error. Additionally, the aggregation accuracy is achieved up to 99.95% with a relative improvement of 23.92%.

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Data Availability Statement

The data are available upon reasonable request to the corresponding authors.

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Acknowledgements

The authors would like to thank the National Institute of Technology, Kurukshetra, India, for financially supporting this research work.

Funding

National Institute of Technology, Kurukshetra, India, funded this research under the Institute fellowship.

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Authors and Affiliations

  1. Department of Computer Applications, National Institute of Technology Kurukshetra, Thanesar, Haryana, 136119, India

    Ashutosh Kumar Singh & Pooja Rani

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  1. Ashutosh Kumar Singh
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  2. Pooja Rani
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Contributions

All the authors have discussed and constructed the ideas, designed the Virtual Machine Placement framework, and wrote the paper together.

Corresponding author

Correspondence to Pooja Rani.

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Singh, A.K., Rani, P. An AI-driven fault-tolerant aggregation model for smart grid. J Supercomput 79, 20665–20683 (2023). https://doi.org/10.1007/s11227-023-05461-3

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