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Solar power forecasting of a residential location as part of a smart grid structure
University of Deusto, Bilbao, Spain .
Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.
University of Deusto, Bilbao, Spain .
2012 (English)In: Energytech 2012, 2012, 1-6 p.Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents the use of an artificial neural network for classification on a residence house that uses local air temperature and solar insulation predictions to identify patterns at the desired location, in order to obtain a stochastic distribution of the daily solar profile. This is a first step on the further creation of a short-term operation model that allows determining the technical and economic impact of stationary/mobile batteries of electric vehicles in presence of microrenewables. This short-term operation model will be in the day-ahead perfect market operation (unit commitment) where specific changes are made to consider stationary and mobile operation. © 2012 IEEE.

Place, publisher, year, edition, pages
2012. 1-6 p.
Series
2012 IEEE Energytech, Energytech 2012
Keyword [en]
artificial neural network; forecasting; microrenewables; smart house; solar
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-100942DOI: 10.1109/EnergyTech.2012.6304674ISBN: 978-1-4673-1836-5 (print)OAI: oai:DiVA.org:liu-100942DiVA: diva2:664643
Conference
2012 IEEE Energytech, 29-31 May 2012, Cleveland, OH, USA
Available from: 2013-11-15 Created: 2013-11-14 Last updated: 2013-11-29Bibliographically approved

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Citation style
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