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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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dc.contributor.author Hernández, Luis es_ES
dc.contributor.author Baladrón Zorita, Carlos es_ES
dc.contributor.author Aguiar Pérez, Javier Manuel es_ES
dc.contributor.author Calavia Domínguez, Lorena es_ES
dc.contributor.author Carro Martínez, Belén es_ES
dc.contributor.author Sanchez-Esguevillas, Antonio es_ES
dc.contributor.author Garcia Fernandez, Pablo es_ES
dc.contributor.author Lloret, Jaime es_ES
dc.date.accessioned 2014-10-10T15:13:27Z
dc.date.available 2014-10-10T15:13:27Z
dc.date.issued 2013-06
dc.identifier.issn 1996-1073
dc.identifier.uri http://hdl.handle.net/10251/43121
dc.description.abstract 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 load forecasting, which opens the door for the implementation of new algorithms to seize this information for optimization and advanced planning. Therefore, accuracy in load forecasts will potentially have a big impact on key operation factors for the future Smart Grid/Microgrid-based energy network like user satisfaction and resource saving, and new methods to achieve an efficient prediction in future energy landscapes (very different from the centralized, big area networks studied so far). This paper proposes different improved models to forecast next day's aggregated load using artificial neural networks, taking into account the variables that are most relevant. In particular, seven models based on the multilayer perceptron will be proposed, progressively adding input variables after analyzing the influence of climate factors on aggregated load. The results section presents the forecast from the proposed models, obtained from real data. es_ES
dc.language Inglés es_ES
dc.publisher MDPI es_ES
dc.relation.ispartof Energies es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject artificial neural network es_ES
dc.subject aggregated load es_ES
dc.subject smart grid es_ES
dc.subject microgrid es_ES
dc.subject multilayer perceptron es_ES
dc.subject.classification ORGANIZACION DE EMPRESAS es_ES
dc.subject.classification INGENIERIA TELEMATICA es_ES
dc.title Experimental Analysis of the Input Variables' Relevance to Forecast Next Day's Aggregated Electric Demand Using Neural Networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/en6062927
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.contributor.affiliation 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 es_ES
dc.description.bibliographicCitation 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 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.3390/en6062927 es_ES
dc.description.upvformatpinicio 2927 es_ES
dc.description.upvformatpfin 2948 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 6 es_ES
dc.description.issue 6 es_ES
dc.relation.senia 265801
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