A hybrid integrated architecture for energy consumption prediction
Por favor, use este identificador para citar o enlazar este ítem:
http://hdl.handle.net/10045/57266
Título: | A hybrid integrated architecture for energy consumption prediction |
---|---|
Autor/es: | Maté, Alejandro | Peral, Jesús | Ferrández, Antonio | Gil, David | Trujillo, Juan |
Grupo/s de investigación o GITE: | Lucentia | Procesamiento del Lenguaje y Sistemas de Información (GPLSI) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos | Universidad de Alicante. Departamento de Tecnología Informática y Computación |
Palabras clave: | Data mining | Energy consumption | Information Extraction | Big data | Decision trees | Social networks |
Área/s de conocimiento: | Lenguajes y Sistemas Informáticos | Arquitectura y Tecnología de Computadores |
Fecha de publicación: | oct-2016 |
Editor: | Elsevier |
Cita bibliográfica: | Future Generation Computer Systems. 2016, 63: 131-147. doi:10.1016/j.future.2016.03.020 |
Resumen: | Irresponsible and negligent use of natural resources in the last five decades has made it an important priority to adopt more intelligent ways of managing existing resources, especially the ones related to energy. The main objective of this paper is to explore the opportunities of integrating internal data already stored in Data Warehouses together with external Big Data to improve energy consumption predictions. This paper presents a study in which we propose an architecture that makes use of already stored energy data and external unstructured information to improve knowledge acquisition and allow managers to make better decisions. This external knowledge is represented by a torrent of information that, in many cases, is hidden across heterogeneous and unstructured data sources, which are recuperated by an Information Extraction system. Alternatively, it is present in social networks expressed as user opinions. Furthermore, our approach applies data mining techniques to exploit the already integrated data. Our approach has been applied to a real case study and shows promising results. The experiments carried out in this work are twofold: (i) using and comparing diverse Artificial Intelligence methods, and (ii) validating our approach with data sources integration. |
Patrocinador/es: | This work has been funded by the Spanish Ministry of Economy and Competitiveness under the project Grant TIN2015-63502-C3-3-R, by the Generalitat Valenciana under the project Prometeo (PROMETEOII/2014/001) and the University of Alicante within the program of support for research under project GRE14-10 (BOUA of 03/06/2014). Alejandro Maté is funded by the Generalitat Valenciana (APOSTD/2014/064). |
URI: | http://hdl.handle.net/10045/57266 |
ISSN: | 0167-739X (Print) | 1872-7115 (Online) |
DOI: | 10.1016/j.future.2016.03.020 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2016 Elsevier B.V. |
Revisión científica: | si |
Versión del editor: | http://dx.doi.org/10.1016/j.future.2016.03.020 |
Aparece en las colecciones: | INV - GPLSI - Artículos de Revistas INV - LUCENTIA - Artículos de Revistas |
Archivos en este ítem:
Archivo | Descripción | Tamaño | Formato | |
---|---|---|---|---|
2016_Mate_etal_FGCS_final.pdf | Versión final (acceso restringido) | 1,69 MB | Adobe PDF | Abrir Solicitar una copia |
2016_Mate_etal_FGCS_accepted.pdf | Accepted Manuscript (acceso abierto) | 1,34 MB | Adobe PDF | Abrir Vista previa |
Todos los documentos en RUA están protegidos por derechos de autor. Algunos derechos reservados.