TY - GEN
T1 - Wind Power Prediction by Using Wavelet Decomposition Mode Based NARX-Neural Network
AU - Prasetyowati, A.
AU - S., Harry Sudibyo
AU - Sudiana, Dodi
N1 - Funding Information:
Our thanks to research funding provided by the PITTA(HIBAH PUBLIKASI INTERNASIONAL TERINDEKS UNTUK TUGAS AKHIR MAHASISWA UI) grant and the New Renewable Energy Society (METI) BPPT for allowing us to use Pandansimo wind farm data to develop into a wind power prediction model
Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/12/5
Y1 - 2017/12/5
N2 - Wind energy predictions have been widely developed with a variety of methods, this is due to the stochastic character and uncertainty in the wind. The need for wind energy generation is so great that it must be prepared for operational prediction on its network. This study is very important that aims to design an algorithm to predict wind power for grid operators that are useful to accelerate the management planning of the generation so that the resulting wind power is more optimal. In this paper, we propose a model of wind power prediction by attaching highly intermittent wind speed behavior that makes wind power change rapidly. To overcome this, Wavelet Decomposition method is proposed, then this model is hybridized using Nonlinear autoregressive using Nonlinear autoregressive modeling machine with exogenous input model Nonlinear Autoregressive with External-Neural Network (NARX-NN). The simulation results show that this model can improve the accuracy performance of previous models using BP-Neural Network.
AB - Wind energy predictions have been widely developed with a variety of methods, this is due to the stochastic character and uncertainty in the wind. The need for wind energy generation is so great that it must be prepared for operational prediction on its network. This study is very important that aims to design an algorithm to predict wind power for grid operators that are useful to accelerate the management planning of the generation so that the resulting wind power is more optimal. In this paper, we propose a model of wind power prediction by attaching highly intermittent wind speed behavior that makes wind power change rapidly. To overcome this, Wavelet Decomposition method is proposed, then this model is hybridized using Nonlinear autoregressive using Nonlinear autoregressive modeling machine with exogenous input model Nonlinear Autoregressive with External-Neural Network (NARX-NN). The simulation results show that this model can improve the accuracy performance of previous models using BP-Neural Network.
KW - Bp-Neural Network
KW - NARX-NN
KW - Wavelet decomposition
KW - Wind Power Prediction
UR - http://www.scopus.com/inward/record.url?scp=85042067951&partnerID=8YFLogxK
U2 - 10.1145/3168390.3168434
DO - 10.1145/3168390.3168434
M3 - Conference contribution
AN - SCOPUS:85042067951
T3 - ACM International Conference Proceeding Series
SP - 275
EP - 278
BT - Proceedings of 2017 International Conference on Computer Science and Artificial Intelligence, CSAI 2017
PB - Association for Computing Machinery
T2 - 2017 International Conference on Computer Science and Artificial Intelligence, CSAI 2017
Y2 - 5 December 2017 through 7 December 2017
ER -