Predicción a corto plazo de sistemas de medición inteligentes mediante arquitecturas de aprendizaje profundo multivariable y multipaso

Jorge Lara; Mauricio Samper; Graciela Colomé

doi:10.37116/revistaenergia.v21.n1.2024.652

Short-Term Prediction of Smart Metering Systems by Multivariable and Multistep Deep Learning Architectures

Predicción a corto plazo de sistemas de medición inteligentes mediante arquitecturas de aprendizaje profundo multivariable y multipaso

Published 2024-07-24

https://doi.org/10.37116/revistaenergia.v21.n1.2024.652

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Vol. 21 No. 1 (2024): Revista Técnica "energía", Edición No. 21, ISSUE I

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TECNOLÓGICOS E INNOVACIÓN

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Short-Term Prediction of Smart Metering Systems by Multivariable and Multistep Deep Learning Architectures . (2024). Revista Técnica "energía", 21(1), PP. 153-164. https://doi.org/10.37116/revistaenergia.v21.n1.2024.652

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https://doi.org/10.37116/revistaenergia.v21.n1.2024.652

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Short-Term Prediction of Smart Metering Systems by Multivariable and Multistep Deep Learning Architectures . (2024). Revista Técnica "energía", 21(1), PP. 153-164. https://doi.org/10.37116/revistaenergia.v21.n1.2024.652

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Jorge Lara

Universidad Nacional de San Juan

https://orcid.org/0000-0003-4035-3524

Mauricio Samper

Universidad Nacional de San Juan

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https://orcid.org/0000-0003-2416-1709

Graciela Colomé

Universidad Nacional de San Juan

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https://orcid.org/0000-0002-2926-5366

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Prediction

Smart Meter

Deep Learning

Smart Grid

Smart Grids have revolutionized the electricity industry by enabling more efficient control and monitoring of the electricity supply, with a key component being smart meters (SM). These collect information on demand, energy, and harmonic distortion, among others, which must be stored and managed efficiently in a metering data management system (MDMS). The MDMS must ensure that a complete set of data is obtained for use in algorithms to ensure the reliability and quality of the power supply. To address the challenge of management the big data generated by SM, short, medium, and long-term measurement forecasting techniques have been proposed, highlighting the use of artificial intelligence such as Artificial Neural Networks (ANN) and Deep Learning (DL) methods due to their ability to adapt to different input and output variables with various time horizons. In addition, the influence of the diversity of Information and Communication Technologies (ICT) on the update time and data storage in the MDMS is highlighted. In this sense, this work aims to identify which ANN or DL architecture(s) could be more suitable for enterprise, survey, or research applications, demonstrating favorable performance metrics in different scenarios of sampling frequency and typical data update times in Smart Grids. This is relevant due to the need for MDMS to perform multivariate and multi-pass predictions in the short term to complete the information until the information is available or updated.

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