Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/64706
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dc.contributor.authorChin, Ken Liang Koon
dc.date.accessioned2015-05-29T07:06:31Z
dc.date.available2015-05-29T07:06:31Z
dc.date.copyright2015en_US
dc.date.issued2015
dc.identifier.urihttp://hdl.handle.net/10356/64706
dc.description.abstractMost countries in the world rely heavily on coal, oil and natural gas for its energy. But they are non-renewable and is currently depleting from its finite resources. This also led to the huge increase of their cost over the years. In contrast, renewable energy are constantly replenished and will never run out. This is why the world is interested in harnessing solar energy as its potential is immense and having environmental advantages. In this project, solar power is the subject of research. As the technology of solar energy advance, higher efficiency and power quality of photovoltaic (PV) power output is being studied. One of the ways is to provide accurate prediction continuously to PV power plants as fast as possible so it will balance its energy dispatch. Forecasting algorithm such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Multiple Linear Regression (MLR) algorithms were being used. The introduction of Extreme Learning Machines (ELM) algorithms have overwhelming by the learning speed and accuracy. Several methods of ELM are used to find the one who gives the best accuracy at the fastest speed.en_US
dc.format.extent46 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systemsen_US
dc.titleExtreme learning machines on PV generation regressionen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorFarank Golestaneh
dc.contributor.supervisorGooi Hoay Bengen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineeringen_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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