An application of artificial neural network on short term load forecasting using back propagation algorithm / Elia Erwani Hassan
This study is covered a new approach to load forecasting using Artificial Neural Network (ANNs). Improving accuracy of load forecast by Back Propagation Algorithm is the main objective for this project. This accuracy is dependent on several ANN parameters such as learning rate and momentum rate. The...
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| 語言: | English |
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1998
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| 在線閱讀: | https://ir.uitm.edu.my/id/eprint/101732/1/101732.pdf |
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my-uitm-ir.1017322024-09-14T07:19:48Z An application of artificial neural network on short term load forecasting using back propagation algorithm / Elia Erwani Hassan 1998 Hassan, Elia Erwani This study is covered a new approach to load forecasting using Artificial Neural Network (ANNs). Improving accuracy of load forecast by Back Propagation Algorithm is the main objective for this project. This accuracy is dependent on several ANN parameters such as learning rate and momentum rate. The Back Propagation Algorithm, which consists of the multi-layered perception model, makes possible to train the ANN training pattems. As an input, we look at the past 24 hours load data with the type of days as weekdays, Sunday and public holidays. The next 24 hours load patters are considered as outputs. By using Back Propagation Algorithm with 25 hidden nodes, 0.7 learning rate and 0.7 momentum rate have been found to give faster result than other conventional techniques. 1998 Thesis https://ir.uitm.edu.my/id/eprint/101732/ https://ir.uitm.edu.my/id/eprint/101732/1/101732.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty Of Electrical Engineering |
| institution |
Universiti Teknologi MARA |
| collection |
UiTM Institutional Repository |
| language |
English |
| description |
This study is covered a new approach to load forecasting using Artificial Neural Network (ANNs). Improving accuracy of load forecast by Back Propagation Algorithm is the main objective for this project. This accuracy is dependent on several ANN parameters such as learning rate and momentum rate. The Back Propagation Algorithm, which consists of the multi-layered perception model, makes possible to train the ANN training pattems. As an input, we look at the past 24 hours load data with the type of days as weekdays, Sunday and public holidays. The next 24 hours load patters are considered as outputs. By using Back Propagation Algorithm with 25 hidden nodes, 0.7 learning rate and 0.7 momentum rate have been found to give faster result than other conventional techniques. |
| format |
Thesis |
| qualification_level |
Bachelor degree |
| author |
Hassan, Elia Erwani |
| spellingShingle |
Hassan, Elia Erwani An application of artificial neural network on short term load forecasting using back propagation algorithm / Elia Erwani Hassan |
| author_facet |
Hassan, Elia Erwani |
| author_sort |
Hassan, Elia Erwani |
| title |
An application of artificial neural network on short term load forecasting using back propagation algorithm / Elia Erwani Hassan |
| title_short |
An application of artificial neural network on short term load forecasting using back propagation algorithm / Elia Erwani Hassan |
| title_full |
An application of artificial neural network on short term load forecasting using back propagation algorithm / Elia Erwani Hassan |
| title_fullStr |
An application of artificial neural network on short term load forecasting using back propagation algorithm / Elia Erwani Hassan |
| title_full_unstemmed |
An application of artificial neural network on short term load forecasting using back propagation algorithm / Elia Erwani Hassan |
| title_sort |
application of artificial neural network on short term load forecasting using back propagation algorithm / elia erwani hassan |
| granting_institution |
Universiti Teknologi MARA (UiTM) |
| granting_department |
Faculty Of Electrical Engineering |
| publishDate |
1998 |
| url |
https://ir.uitm.edu.my/id/eprint/101732/1/101732.pdf |
| _version_ |
1811769188161683456 |