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    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/56883

    Título
    Artificial neural network for short-term load forecasting in distribution systems
    Autor
    Hernández Callejo, LuisAutoridad UVA Orcid
    Baladrón García, CarlosAutoridad UVA
    Aguiar Pérez, Javier ManuelAutoridad UVA Orcid
    Calavia, Lorena
    Carro Martínez, BelénAutoridad UVA Orcid
    Sánchez Esguevillas, Antonio JavierAutoridad UVA Orcid
    Pérez, Francisco
    Fernández, Ángel
    Lloret, Jaime
    Año del Documento
    2014
    Editorial
    MDPI
    Descripción
    Producción Científica
    Documento Fuente
    Energies, 2014, vol. 7, n. 3, p. 1576-1598
    Resumen
    The new paradigms and latest developments in the Electrical Grid are based on the introduction of distributed intelligence at several stages of its physical layer, giving birth to concepts such as Smart Grids, Virtual Power Plants, microgrids, Smart Buildings and Smart Environments. Distributed Generation (DG) is a philosophy in which energy is no longer produced exclusively in huge centralized plants, but also in smaller premises which take advantage of local conditions in order to minimize transmission losses and optimize production and consumption. This represents a new opportunity for renewable energy, because small elements such as solar panels and wind turbines are expected to be scattered along the grid, feeding local installations or selling energy to the grid depending on their local generation/consumption conditions. The introduction of these highly dynamic elements will lead to a substantial change in the curves of demanded energy. The aim of this paper is to apply Short-Term Load Forecasting (STLF) in microgrid environments with curves and similar behaviours, using two different data sets: the first one packing electricity consumption information during four years and six months in a microgrid along with calendar data, while the second one will be just four months of the previous parameters along with the solar radiation from the site. For the first set of data different STLF models will be discussed, studying the effect of each variable, in order to identify the best one. That model will be employed with the second set of data, in order to make a comparison with a new model that takes into account the solar radiation, since the photovoltaic installations of the microgrid will cause the power demand to fluctuate depending on the solar radiation.
    Materias Unesco
    33 Ciencias Tecnológicas
    Palabras Clave
    Microgrid
    Short-term electric load forecasting
    Multi-layer perceptron
    Artificial neural network
    Neural networks
    ISSN
    1996-1073
    Revisión por pares
    SI
    DOI
    10.3390/en7031576
    Patrocinador
    Ministerio de Economía y Competitividad, convenio INNPACTO - proyecto MIRED-CON (IPT-2012-0611-120000)
    Version del Editor
    https://www.mdpi.com/1996-1073/7/3/1576
    Propietario de los Derechos
    © 2014 The Author(s)
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/56883
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    openAccess
    Aparece en las colecciones
    • DEP71 - Artículos de revista [358]
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    Artificial-neural-network-short-term.pdf
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    Attribution 3.0 UnportedLa licencia del ítem se describe como Attribution 3.0 Unported

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