Load prediction using artificial neural network (ANN) / Mohammad Tariq Zakaria
The purpose of this project is to study and develop an artificial neural network (ANN) model specifically for short term load prediction. A nonlinear load model is proposed and several structures of ANN for short term load prediction are tested. The outputs obtained were the predicted full day load...
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2012
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| Online Access: | https://ir.uitm.edu.my/id/eprint/84796/1/84796.pdf https://ir.uitm.edu.my/id/eprint/84796/ |
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my.uitm.ir.847962024-01-12T09:16:14Z https://ir.uitm.edu.my/id/eprint/84796/ Load prediction using artificial neural network (ANN) / Mohammad Tariq Zakaria Zakaria, Mohammad Tariq The purpose of this project is to study and develop an artificial neural network (ANN) model specifically for short term load prediction. A nonlinear load model is proposed and several structures of ANN for short term load prediction are tested. The outputs obtained were the predicted full day load demand for the next day or week. The ANN model has 4 layers; an input layer, two hidden layers and an output layer. The number of inputs was 6; while the number of hidden layer neurons was varied for different performance of the network. The output layer has 24 neurons. The ANN model was trained for over 5 weeks. A mean absolute percentage errors of 2.52% was achieved when the trained network was tested on random for one week's data. 2012 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/84796/1/84796.pdf Load prediction using artificial neural network (ANN) / Mohammad Tariq Zakaria. (2012) Degree thesis, thesis, Universiti Teknologi MARA (UiTM). |
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The purpose of this project is to study and develop an artificial neural network (ANN) model specifically for short term load prediction. A nonlinear load model is proposed and several structures of ANN for short term load prediction are tested. The outputs obtained were the predicted full day load demand for the next day or week. The ANN model has 4 layers; an input layer, two hidden layers and an output layer. The number of inputs was 6; while the number of hidden layer neurons was varied for different performance of the network. The output layer has 24 neurons. The ANN model was trained for over 5 weeks. A mean absolute percentage errors of 2.52% was achieved when the trained network was tested on random for one week's data. |
| format |
Thesis |
| author |
Zakaria, Mohammad Tariq |
| spellingShingle |
Zakaria, Mohammad Tariq Load prediction using artificial neural network (ANN) / Mohammad Tariq Zakaria |
| author_facet |
Zakaria, Mohammad Tariq |
| author_sort |
Zakaria, Mohammad Tariq |
| title |
Load prediction using artificial neural network (ANN) / Mohammad Tariq Zakaria |
| title_short |
Load prediction using artificial neural network (ANN) / Mohammad Tariq Zakaria |
| title_full |
Load prediction using artificial neural network (ANN) / Mohammad Tariq Zakaria |
| title_fullStr |
Load prediction using artificial neural network (ANN) / Mohammad Tariq Zakaria |
| title_full_unstemmed |
Load prediction using artificial neural network (ANN) / Mohammad Tariq Zakaria |
| title_sort |
load prediction using artificial neural network (ann) / mohammad tariq zakaria |
| publishDate |
2012 |
| url |
https://ir.uitm.edu.my/id/eprint/84796/1/84796.pdf https://ir.uitm.edu.my/id/eprint/84796/ |
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