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Smart city article details

Title	Addressing Data Scarcity In Local Photovoltaic Datasets: A Gan-Based Workflow
ID_Doc	6397
Authors	Izzi F.; Martella C.; Longo A.
Year	2024
Published	Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024
DOI	http://dx.doi.org/10.1109/BigData62323.2024.10825752
Abstract	Digital twins are increasingly being implemented in smart cities, where they are designed to provide 3D digital representations for near-real time interactivity and status feedback of the twinned physical assets. The ability to provide accurate predictions in what-if scenarios plays a key role in driving the advances in resources optimization and risk mitigation. To this end, prediction models require large datasets to train, and such datasets are usually scarce at local level. In this paper, we propose a generative AI-based workflow to infer domestic photovoltaic energy production data, based on a process that uses Generative Adversarial Networks to generate an enriched meteorological dataset. The generated meteorological data captures essential statistical properties of real data that can be later used to infer realistic energy production data points. The resulting output dataset is validated against a real photovoltaic system, highlighting the ability to deliver high-fidelity time series out of scarce input datasets. © 2024 IEEE.
Author Keywords	data augmentation; data scarcity; digital twin; Generative Adversarial Networks; smart city

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