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Neural network controller for Active Demand-Side Management with PV energy in the residential sector

descriptionPublicationkeyboard_double_arrow_right Article , Journal 01 Mar 2012 Spain Publisher:Elsevier BVJournal:Applied Energy, volume 91, pages 90-97 (issn: 0306-2619,

Authors: Matallanas de Avila, Eduardo; Castillo Cagigal, Manuel; Gutiérrez Martín, Álvaro; Monasterio-Huelin Maciá, Felix; Caamaño Martín, Estefanía; Masa Bote, Daniel; Jiménez Leube, Francisco Javier;

doi: 10.1016/j.apenergy.2011.09.004

Neural network controller for Active Demand-Side Management with PV energy in the residential sector

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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.

Country

Spain

Related Organizations

Universidad Politécnica de Madrid
Spain

Keywords

Telecomunicaciones, Energía Eléctrica

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	185
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 1%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 1%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 1%