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Development of Wind Mapping Based on Artificial Neural Network (ANN) for Energy Exploration in Sarawak

The exponential rise in global population and rapidly depleting reserves of fossil fuels and pollution that is occurring as a result of burning hydrocarbons have drawn the attention of researchers, engineers and designers in searching for clean and emission free sources of energy. Wind energy is na...

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Main Authors: Lawan, S.M., Abidin, W.A.W.Z., Chai, W.Y, Baharun, A., Masri, T.
Format: E-Article
Language:English
Published: International Journal Of Renewable Energy Research 2014
Subjects:
TD Environmental technology. Sanitary engineering
Online Access:http://ir.unimas.my/id/eprint/5197/1/development%20of%20wind%20mapping%20based%20on%20artificial%20neural%20network%20%28abstract%29.pdf
http://ir.unimas.my/id/eprint/5197/
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spelling my.unimas.ir.51972016-04-14T00:16:43Z http://ir.unimas.my/id/eprint/5197/ Development of Wind Mapping Based on Artificial Neural Network (ANN) for Energy Exploration in Sarawak Lawan, S.M. Abidin, W.A.W.Z. Chai, W.Y Baharun, A. Masri, T. TD Environmental technology. Sanitary engineering The exponential rise in global population and rapidly depleting reserves of fossil fuels and pollution that is occurring as a result of burning hydrocarbons have drawn the attention of researchers, engineers and designers in searching for clean and emission free sources of energy. Wind energy is naturally replenished which comes from wind and produce electricity using natural power of wind to drive a generator. The power is clean and inexhaustible that will sustain and maintained the environment. The most important parameter of the wind energy is the wind velocity. A couple number of wind speed prediction models have been published in scientific literatures that are related to estimation of wind speed values. This paper presents Neural Network (NN) techniques for the prediction of wind speed in the areas where wind speeds velocity does not exist. The ANN model has been designed using the NN Toolbox in Matlab environment. A total of ten years data from five locations starting from 2003 to 2012, and five years data from a period of 2008-2012 were used for the network training, testing and validation. Topographical parameters (latitude, longitude and elevation) and meteorological variables that results in wind formation have been considered in this study. Comparison techniques based on statistical measures between the references measured and simulated wind speed indicated that the ANN model correlated well with reference measured data. International Journal Of Renewable Energy Research 2014 E-Article PeerReviewed text en http://ir.unimas.my/id/eprint/5197/1/development%20of%20wind%20mapping%20based%20on%20artificial%20neural%20network%20%28abstract%29.pdf Lawan, S.M. and Abidin, W.A.W.Z. and Chai, W.Y and Baharun, A. and Masri, T. (2014) Development of Wind Mapping Based on Artificial Neural Network (ANN) for Energy Exploration in Sarawak. International Journal Of Renewable Energy Research, 4 (3).
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic TD Environmental technology. Sanitary engineering
spellingShingle TD Environmental technology. Sanitary engineering
Lawan, S.M.
Abidin, W.A.W.Z.
Chai, W.Y
Baharun, A.
Masri, T.
Development of Wind Mapping Based on Artificial Neural Network (ANN) for Energy Exploration in Sarawak
description The exponential rise in global population and rapidly depleting reserves of fossil fuels and pollution that is occurring as a result of burning hydrocarbons have drawn the attention of researchers, engineers and designers in searching for clean and emission free sources of energy. Wind energy is naturally replenished which comes from wind and produce electricity using natural power of wind to drive a generator. The power is clean and inexhaustible that will sustain and maintained the environment. The most important parameter of the wind energy is the wind velocity. A couple number of wind speed prediction models have been published in scientific literatures that are related to estimation of wind speed values. This paper presents Neural Network (NN) techniques for the prediction of wind speed in the areas where wind speeds velocity does not exist. The ANN model has been designed using the NN Toolbox in Matlab environment. A total of ten years data from five locations starting from 2003 to 2012, and five years data from a period of 2008-2012 were used for the network training, testing and validation. Topographical parameters (latitude, longitude and elevation) and meteorological variables that results in wind formation have been considered in this study. Comparison techniques based on statistical measures between the references measured and simulated wind speed indicated that the ANN model correlated well with reference measured data.
format E-Article
author Lawan, S.M.
Abidin, W.A.W.Z.
Chai, W.Y
Baharun, A.
Masri, T.
author_facet Lawan, S.M.
Abidin, W.A.W.Z.
Chai, W.Y
Baharun, A.
Masri, T.
author_sort Lawan, S.M.
title Development of Wind Mapping Based on Artificial Neural Network (ANN) for Energy Exploration in Sarawak
title_short Development of Wind Mapping Based on Artificial Neural Network (ANN) for Energy Exploration in Sarawak
title_full Development of Wind Mapping Based on Artificial Neural Network (ANN) for Energy Exploration in Sarawak
title_fullStr Development of Wind Mapping Based on Artificial Neural Network (ANN) for Energy Exploration in Sarawak
title_full_unstemmed Development of Wind Mapping Based on Artificial Neural Network (ANN) for Energy Exploration in Sarawak
title_sort development of wind mapping based on artificial neural network (ann) for energy exploration in sarawak
publisher International Journal Of Renewable Energy Research
publishDate 2014
url http://ir.unimas.my/id/eprint/5197/1/development%20of%20wind%20mapping%20based%20on%20artificial%20neural%20network%20%28abstract%29.pdf
http://ir.unimas.my/id/eprint/5197/
_version_ 1644509746648055808
score 13.211869

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