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Experimental Analysis of the Input Variables' Relevance to Forecast Next Day's Aggregated Electric Demand Using Neural Networks

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Experimental Analysis of the Input Variables' Relevance to Forecast Next Day's Aggregated Electric Demand Using Neural Networks

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Hernández, L.; Baladrón Zorita, C.; Aguiar Pérez, JM.; Calavia Domínguez, L.; Carro Martínez, B.; Sanchez-Esguevillas, A.; Garcia Fernandez, P.... (2013). Experimental Analysis of the Input Variables' Relevance to Forecast Next Day's Aggregated Electric Demand Using Neural Networks. Energies. 6(6):2927-2948. doi:10.3390/en6062927

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Título: Experimental Analysis of the Input Variables' Relevance to Forecast Next Day's Aggregated Electric Demand Using Neural Networks
Autor: Hernández, Luis Baladrón Zorita, Carlos Aguiar Pérez, Javier Manuel Calavia Domínguez, Lorena Carro Martínez, Belén Sanchez-Esguevillas, Antonio Garcia Fernandez, Pablo Lloret, Jaime
Entidad UPV: Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Universitat Politècnica de València. Instituto de Investigación para la Gestión Integral de Zonas Costeras - Institut d'Investigació per a la Gestió Integral de Zones Costaneres
Fecha difusión:
Resumen:
Thanks to the built in intelligence (deployment of new intelligent devices and sensors in places where historically they were not present), the Smart Grid and Microgrid paradigms are able to take advantage from aggregated ...[+]
Palabras clave: artificial neural network , aggregated load , smart grid , microgrid , multilayer perceptron
Derechos de uso: Reconocimiento (by)
Fuente:
Energies. (issn: 1996-1073 )
DOI: 10.3390/en6062927
Editorial:
MDPI
Versión del editor: http://dx.doi.org/10.3390/en6062927
Tipo: Artículo

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