Title :
Solar power forecasting of a residential location as part of a smart grid structure
Author :
Ioakimidis, C.S. ; Eliasstam, H. ; Rycerski, P.
Author_Institution :
Energy Unit, Univ. of Deusto, Bilbao, Spain
Abstract :
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.
Keywords :
battery powered vehicles; building integrated photovoltaics; insulation; load forecasting; neural nets; pattern classification; power engineering computing; power generation dispatch; power generation scheduling; power markets; smart power grids; solar power stations; statistical distributions; stochastic processes; artificial neural network; daily solar profile; day-ahead perfect market operation; electric vehicles; insulation predictions; local air temperature; microrenewable energy resources; mobile battery; residence house; short-term operation model; smart grid structure; solar power forecasting; stochastic distribution; unit commitment; Artificial neural networks; Biological neural networks; Electricity; Estimation; Insulation; Mathematical model; Photovoltaic cells; artificial neural network; forecasting; microrenewables; smart house; solar;
Conference_Titel :
Energytech, 2012 IEEE
Conference_Location :
Cleveland, OH
Print_ISBN :
978-1-4673-1836-5
Electronic_ISBN :
978-1-4673-1834-1
DOI :
10.1109/EnergyTech.2012.6304674
