Logo Repositorio Institucional

Please use this identifier to cite or link to this item: http://dspace.ucuenca.edu.ec/handle/123456789/36601
Title: A comprehensive solution for electrical energy demand prediction based on auto-regressive models
Authors: Saenz Peñafiel, Juan Jose
Luzuriaga, Jorge E.
Lemus Zúñiga, Lenin Guillermo
Solis Cabrera, Vanessa Alexandra
metadata.dc.ucuenca.correspondencia: Saenz Peñafiel, Juan Jose, juan.saenz@ucuenca.edu.ec
Keywords: Prediction
Energy demand
Data capture
Auto-regressive models
metadata.dc.ucuenca.areaconocimientofrascatiamplio: 2. Ingeniería y Tecnología
metadata.dc.ucuenca.areaconocimientofrascatidetallado: 2.2.4 Ingeniería de La Comunicación y de Sistemas
metadata.dc.ucuenca.areaconocimientofrascatiespecifico: 2.2 Ingenierias Eléctrica, Electrónica e Información
metadata.dc.ucuenca.areaconocimientounescoamplio: 06 - Información y Comunicación (TIC)
metadata.dc.ucuenca.areaconocimientounescodetallado: 0613 - Software y Desarrollo y Análisis de Aplicativos
metadata.dc.ucuenca.areaconocimientounescoespecifico: 061 - Información y Comunicación (TIC)
Issue Date: 2020
metadata.dc.ucuenca.volumen: Volumen 1273
metadata.dc.source: Systems and Information Sciences
metadata.dc.identifier.doi: 10.1007/978-3-030-59194-6_36
Publisher: Springer Nature
Energy consumption and demand are two widely used terms necessary to understand the functioning of the different mechanisms used in electrical energy transactions. In this article, the design and construction of a comprehensive solution to forecast future trends in electricity transactions using the historical data and two auto-regressive models were considered. Simple linear regression and a complete model such as ARIMA. We compared these models to find which one best suits the type of data considering their strengths and weaknesses for this specific case. Finally, to complete the comprehensive solution, the results are presented to the final user. This solution is mainly aimed at professionals who carry out activities related to contracting and managing electricity supply in public institutions. This solution pretends to collaborate to reduce energy demand and therefore, consumption.
URI: https://www.scopus.com/record/display.uri?eid=2-s2.0-85094118561&origin=resultslist&sort=plf-f&src=s&sid=602f52359427b9118ad266ea4b25e539&sot=b&sdt=b&sl=111&s=TITLE-ABS-KEY%28A+Comprehensive+Solution+for+Electrical+Energy+Demand+Prediction+Based+on+Auto-Regressive+Models%29&relpos=0&citeCnt=0&searchTerm=
metadata.dc.ucuenca.urifuente: https://link.springer.com/book/10.1007/978-3-030-59194-6
ISBN: 978-3-030-59194-6
ISSN: 0000-0000
Appears in Collections:Artículos

Files in This Item:
File Description SizeFormat 
documento.pdfdocument113.55 kBAdobe PDFThumbnail

This item is protected by original copyright

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


Centro de Documentacion Regional "Juan Bautista Vázquez"

Biblioteca Campus Central Biblioteca Campus Salud Biblioteca Campus Yanuncay
Av. 12 de Abril y Calle Agustín Cueva, Telf: 4051000 Ext. 1311, 1312, 1313, 1314. Horario de atención: Lunes-Viernes: 07H00-21H00. Sábados: 08H00-12H00 Av. El Paraíso 3-52, detrás del Hospital Regional "Vicente Corral Moscoso", Telf: 4051000 Ext. 3144. Horario de atención: Lunes-Viernes: 07H00-19H00 Av. 12 de Octubre y Diego de Tapia, antiguo Colegio Orientalista, Telf: 4051000 Ext. 3535 2810706 Ext. 116. Horario de atención: Lunes-Viernes: 07H30-19H00