Neural network controller for active demand side management with PV energy in the residential sector

Matallanas de Avila, Eduardo and Castillo Cagigal, Manuel and Gutiérrez Martín, Álvaro and Monasterio-Huelin Maciá, Felix and Caamaño Martín, Estefanía and Masa Bote, Daniel and Jiménez Leube, Francisco Javier (2012). Neural network controller for active demand side management with PV energy in the residential sector. "Applied Energy", v. 91 (n. 1); pp. 90-97. ISSN 0306-2619. https://doi.org/10.1016/j.apenergy.2011.09.004.

Description

Title: Neural network controller for active demand side management with PV energy in the residential sector
Author/s:
  • Matallanas de Avila, Eduardo
  • Castillo Cagigal, Manuel
  • Gutiérrez Martín, Álvaro
  • Monasterio-Huelin Maciá, Felix
  • Caamaño Martín, Estefanía
  • Masa Bote, Daniel
  • Jiménez Leube, Francisco Javier
Item Type: Article
Título de Revista/Publicación: Applied Energy
Date: March 2012
ISSN: 0306-2619
Volume: 91
Subjects:
Freetext Keywords: Demand-Side Management; Distributed energy; PV systems; Control system; Artificial Neural Network; Genetic algorithm
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Tecnologías Especiales Aplicadas a la Aeronáutica [hasta 2014]
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In this paper, we describe the development of a control system for Demand-Side Management in the residential sector with Distributed Generation. The electrical system under study incorporates local PV energy generation, an electricity storage system, connection to the grid and a home automation system. The distributed control system is composed of two modules: a scheduler and a coordinator, both implemented with neural networks. The control system enhances the local energy performance, scheduling the tasks demanded by the user and maximizing the use of local generation.

More information

Item ID: 16855
DC Identifier: http://oa.upm.es/16855/
OAI Identifier: oai:oa.upm.es:16855
DOI: 10.1016/j.apenergy.2011.09.004
Official URL: http://www.sciencedirect.com/science/article/pii/S0306261911005630
Deposited by: Memoria Investigacion
Deposited on: 10 Aug 2013 08:53
Last Modified: 21 Apr 2016 17:12
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