Extreme learning machines on PV generation regression

Most 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 ne...

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Main Author: Chin, Ken Liang Koon
Other Authors: Farank Golestaneh
Format: Final Year Project
Language:English
Published: 2015
Subjects:
Online Access:http://hdl.handle.net/10356/64706
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-647062023-07-07T17:02:16Z Extreme learning machines on PV generation regression Chin, Ken Liang Koon Farank Golestaneh Gooi Hoay Beng School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems Most 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. Bachelor of Engineering 2015-05-29T07:06:31Z 2015-05-29T07:06:31Z 2015 2015 Final Year Project (FYP) http://hdl.handle.net/10356/64706 en Nanyang Technological University 46 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Chin, Ken Liang Koon
Extreme learning machines on PV generation regression
description Most 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.
author2 Farank Golestaneh
author_facet Farank Golestaneh
Chin, Ken Liang Koon
format Final Year Project
author Chin, Ken Liang Koon
author_sort Chin, Ken Liang Koon
title Extreme learning machines on PV generation regression
title_short Extreme learning machines on PV generation regression
title_full Extreme learning machines on PV generation regression
title_fullStr Extreme learning machines on PV generation regression
title_full_unstemmed Extreme learning machines on PV generation regression
title_sort extreme learning machines on pv generation regression
publishDate 2015
url http://hdl.handle.net/10356/64706
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