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Solar power forecasting of a residential location as part of a smart grid structure
University of Deusto, Bilbao, Spain .
Linköpings universitet, Institutionen för systemteknik. Linköpings universitet, Tekniska högskolan.
University of Deusto, Bilbao, Spain .
2012 (engelsk)Inngår i: Energytech 2012, 2012, s. 1-6Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2012. s. 1-6
Serie
2012 IEEE Energytech, Energytech 2012
Emneord [en]
artificial neural network; forecasting; microrenewables; smart house; solar
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-100942DOI: 10.1109/EnergyTech.2012.6304674ISBN: 978-1-4673-1836-5 (tryckt)OAI: oai:DiVA.org:liu-100942DiVA, id: diva2:664643
Konferanse
2012 IEEE Energytech, 29-31 May 2012, Cleveland, OH, USA
Tilgjengelig fra: 2013-11-15 Laget: 2013-11-14 Sist oppdatert: 2013-11-29bibliografisk kontrollert

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Totalt: 14 treff
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