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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| Format: | Article |
| Language: | English |
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International Journal Of Renewable Energy Research
2014
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| Online Access: | http://ir.unimas.my/id/eprint/5197/ http://ir.unimas.my/id/eprint/5197/1/development%20of%20wind%20mapping%20based%20on%20artificial%20neural%20network%20%28abstract%29.pdf |
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| 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. |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T06:10:32Z |
| format | Article |
| id | unimas-5197 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:10:32Z |
| publishDate | 2014 |
| publisher | International Journal Of Renewable Energy Research |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-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 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). |
| 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 |
| title | 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_short | 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 |
| topic | TD Environmental technology. Sanitary engineering |
| url | http://ir.unimas.my/id/eprint/5197/ http://ir.unimas.my/id/eprint/5197/1/development%20of%20wind%20mapping%20based%20on%20artificial%20neural%20network%20%28abstract%29.pdf |