Forecast electricity consumption in Malaysia using artificial intelligence / Muhamad Farhan Abd Rahim
This project focuses on forecast of electricity consumption in Malaysia using artificial intelligence. From the world market, electricity consumption depends on the electrical usage of a bunch of society. Electricity consumption should correspond to the current demand because the production of exces...
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2013
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| 在線閱讀: | https://ir.uitm.edu.my/id/eprint/84794/1/84794.pdf |
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my-uitm-ir.847942024-07-30T00:19:50Z Forecast electricity consumption in Malaysia using artificial intelligence / Muhamad Farhan Abd Rahim 2013 Abd Rahim, Muhamad Farhan Back propagation (Artificial intelligence) Neural networks (Computer science) This project focuses on forecast of electricity consumption in Malaysia using artificial intelligence. From the world market, electricity consumption depends on the electrical usage of a bunch of society. Electricity consumption should correspond to the current demand because the production of excess electricity and the reduction of electricity can cause economic loss. Almost of the large scale, it is impossible to do complete inspection because the time and cost increases drastically with increase in number of samples. This has created a need for a system that can inspect the components automatically with less cost and less time. The ANN will generate the pattern and predict the future pattern of electricity consumption. To improve the result of ANN model, the optimization method was used to optimize the forecast. As the result, the range of electricity consumption is obtained. 2013 Thesis https://ir.uitm.edu.my/id/eprint/84794/ https://ir.uitm.edu.my/id/eprint/84794/1/84794.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Johari, Dalina |
| institution |
Universiti Teknologi MARA |
| collection |
UiTM Institutional Repository |
| language |
English |
| advisor |
Johari, Dalina |
| topic |
Back propagation (Artificial intelligence) Neural networks (Computer science) |
| spellingShingle |
Back propagation (Artificial intelligence) Neural networks (Computer science) Abd Rahim, Muhamad Farhan Forecast electricity consumption in Malaysia using artificial intelligence / Muhamad Farhan Abd Rahim |
| description |
This project focuses on forecast of electricity consumption in Malaysia using artificial intelligence. From the world market, electricity consumption depends on the electrical usage of a bunch of society. Electricity consumption should correspond to the current demand because the production of excess electricity and the reduction of electricity can cause economic loss. Almost of the large scale, it is impossible to do complete inspection because the time and cost increases drastically with increase in number of samples. This has created a need for a system that can inspect the components automatically with less cost and less time. The ANN will generate the pattern and predict the future pattern of electricity consumption. To improve the result of ANN model, the optimization method was used to optimize the forecast. As the result, the range of electricity consumption is obtained. |
| format |
Thesis |
| qualification_level |
Bachelor degree |
| author |
Abd Rahim, Muhamad Farhan |
| author_facet |
Abd Rahim, Muhamad Farhan |
| author_sort |
Abd Rahim, Muhamad Farhan |
| title |
Forecast electricity consumption in Malaysia using artificial intelligence / Muhamad Farhan Abd Rahim |
| title_short |
Forecast electricity consumption in Malaysia using artificial intelligence / Muhamad Farhan Abd Rahim |
| title_full |
Forecast electricity consumption in Malaysia using artificial intelligence / Muhamad Farhan Abd Rahim |
| title_fullStr |
Forecast electricity consumption in Malaysia using artificial intelligence / Muhamad Farhan Abd Rahim |
| title_full_unstemmed |
Forecast electricity consumption in Malaysia using artificial intelligence / Muhamad Farhan Abd Rahim |
| title_sort |
forecast electricity consumption in malaysia using artificial intelligence / muhamad farhan abd rahim |
| granting_institution |
Universiti Teknologi MARA (UiTM) |
| granting_department |
Faculty of Electrical Engineering |
| publishDate |
2013 |
| url |
https://ir.uitm.edu.my/id/eprint/84794/1/84794.pdf |
| _version_ |
1811768703456378880 |