PERAMALAN BEBAN PUNCAK LISTRIK JANGKA PENDEK MENGGUNAKAN METODE JARINGAN SYARAF TIRUAN BACKPROPAGATION
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Institusion

Universitas Sriwijaya

Author

ADITYA, DIMAS
Zaini, Syamsuri
Thayib, Rudyanto

Subject

TK1 Electrical engineering--Periodicals. Automatic control--Periodicals. Computer science--Periodicals. Information technology--Periodicals. Automatic control. Computer science. Electrical engineering. Information technology. 

Datestamp

2023-04-13 01:17:24 

Abstract :
Indonesia is a developing country with all the developments in every sector and is also supported by technological advances. Without an exact formula that can determine the magnitude of the electrical load at any time, what can be done is to predict the electrical load. The load forecasting method discussed in this thesis is the Backpropagation Artificial Neural Network (ANN) method. After the simulation, a comparison is made between the forecast results by the Backpropagation Neural Network and the load coefficient results showing that the average error with the Backpropagation ANN method for the first until fourth weeks reaches 1.89% with an accuracy of 98.11% and the average error of the load coefficient method for the first and second weeks reached 4.51% with an accuracy of 95.49%. These results indicate that the Backpropagation ANN forecasting is better than the load coefficient method 

File :
RAMA_20201_03041281823060.pdf
RAMA_20201_03041281823060_TURNITIN.pdf
RAMA_20201_03041281823060_0004035804_ 0006015605_01_front_ref.pdf
RAMA_20201_03041281823060_0004035804_ 0006015605_02.pdf
RAMA_20201_03041281823060_0004035804_ 0006015605_03.pdf
RAMA_20201_03041281823060_0004035804_ 0006015605_04.pdf
RAMA_20201_03041281823060_0004035804_ 0006015605_05.pdf
RAMA_20201_03041281823060_0004035804_ 0006015605_06_ref.pdf
RAMA_20201_03041281823060_0004035804_ 0006015605_07_lamp.pdf