| 000 | 01915nam a2200253 a 4500 | ||
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| 001 | vtls000055190 | ||
| 003 | KUKTEM | ||
| 005 | 20251114204452.0 | ||
| 008 | 110804t2009 my a f m 000 0 eng d | ||
| 020 | _aTHE0006681(Local) | ||
| 039 | 9 |
_a201905171506 _bamirul _y201108041004 _zFida |
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| 040 | _aUMP | ||
| 090 | _aTK1005 .T35 2009 rs Bc. | ||
| 100 | 1 | _aTai, Hein Fong | |
| 245 | 1 | 0 |
_aCase study of short-term electricity load forecasting with temperature dependency / _cTai Hein Fong |
| 246 | 3 |
_aCase study of short-term electricity load forecasting with temperature dependency _h[computer file] |
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| 260 |
_aKuantan, Pahang : _bUMP, _c2009 |
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| 300 |
_axiv, 112 p. : _bill. (some col.) ; _c30 cm. + _e1 computer disc |
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| 502 | _aProject paper (Bachelor of Electrical Engineering (Power System)) -- Universiti Malaysia Pahang - 2009 | ||
| 520 | 3 | _aLoad forecasting is very essential to the operation of electricity companies. It enhances the energy-efficient and reliable operation of a power system. This is a case study of short-term load forecasting using Artificial Neural Networks (ANNs). This load forecasting program gives load forecasts half an hour in advance. Historical load data obtained from the electricity generation company will be use. The main stages are the pre-processing of the data sets, network training, and forecasting. The inputs used for the neural network are one set of historical load demand data and five sets of temperature data. The neural network used has 3 layers: an input, a hidden, and an output layer. The input layer has 5 neurons, the number of hidden layer neurons can be varied for the different performance of the network, while the output layer has a single neuron. | |
| 650 | 0 |
_aElectric power-plants _xLoad _xForecasting |
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| 650 | 0 | _aElectric power systems | |
| 999 |
_aVIRTUA40 _c2569 _d2575 |
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| 999 | _aVTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5200*6500*6501*9992 | ||