• Tidak ada hasil yang ditemukan

S TE 1100243 Bibliograpy

N/A
N/A
Protected

Academic year: 2017

Membagikan "S TE 1100243 Bibliograpy"

Copied!
4
0
0

Teks penuh

(1)

46

Kartika Ainur Rohmah, 2016

OPTIMAL ANOMALOUS SHORT TERM LOAD FORECASTING BERBASIS ALGORITMA FEED FORWARD BACKPROPAGATION

Universitas Pendidikan Indonesia | repository.upi.edu | perpustakaan.upi.edu

DAFTAR PUSTAKA

Abdullah, A. G., Fitriani, N., Kustija, J., & Kustiawan, I. (2010). Aplikasi Algoritma Feed Forward Backpropagation pada Sistem Keamanan Akses

Menggunakan Sidik Jari. Seminar Dan Workshop Nasional Pendidikan

Teknik Elektro, 112–119.

Aggarwal, L., Aggarwal, K., & Jill, R. (2014). Use of artificial neural networks for the development of an inverse kinematic solution and visual identification

of singularity zone ( s ). Procedia CIRP, 17, 812–817.

http://doi.org/10.1016/j.procir.2014.01.107

Ahmmed, S., Khan, M. A. A., Hasan, K., Saber, A. Y., Nurul, M., & Rahman, M. Z. (2010). STLF Using Neural Networks and Fuzzy for Anomalous Load

Scenarios – A Case Study for Hajj. 6th International Conference on

Electrical and Computer Engineering, (December), 18–20.

Alfina, T., Santosa, B., & Barakbah, R. (2012). Analisa Perbandingan Metode Hierarchical Clustering , K-means dan Gabungan Keduanya dalam Cluster

Data ( Studi kasus : Problem Kerja Praktek Jurusan Teknik Industri ITS ).

JURNAL TEKNIK ITS, 1, A–521–A–525.

Ariana, M. A., Vaferi, B., & Karimi, G. (2015). Prediction of Thermal Conductivity of Alumina Water-Based Nanofluids by Artificial Neural

Networks. Powder Technology, 278, 1–10.

http://doi.org/10.1016/j.powtec.2015.03.005

Arora, S., & Taylor, J. W. (2013). Short-Term Forecasting of Anomalous Load

Using Rule-Based Triple Seasonal Methods. IEEE Transaction on Power

System, 28(3), 3235–3242.

Bichpuriya, Y. K., Member, S., Fernandes, R. S. S., & Soman, S. A. (2012).

Identification of Anomalous Load Profile for Short. IEEE.

Chaturvedi, D. K., Sinha, A. P., & Malik, O. P. (2015). Short Term Load Forecast Using Fuzzy Logic and Wavelet Transform Integrated Generalized Neural

Network. International Journal of Electrical Power and Energy Systems, 67,

230–237. http://doi.org/10.1016/j.ijepes.2014.11.027

Chicco, G., Napoli, R., & Piglione, F. (2001). Load Pattern Clustering for

Short-Term Load Forecasting of Anomalous Days. IEEE Porto Power Tech

Conference.

(2)

47

Kartika Ainur Rohmah, 2016

OPTIMAL ANOMALOUS SHORT TERM LOAD FORECASTING BERBASIS ALGORITMA FEED FORWARD BACKPROPAGATION

Universitas Pendidikan Indonesia | repository.upi.edu | perpustakaan.upi.edu

Networks. Neurocomputing, 71, 3640–3643.

http://doi.org/10.1016/j.neucom.2008.04.004

Comput, J. P. D., Soliman, M. I., & Mohamed, S. A. (2008). A Highly Efficient Implementation of A Backpropagation Learning Algorithm Using Matrix

ISA. J. Parallel Distrib. Comput., 68, 949–961.

http://doi.org/10.1016/j.jpdc.2007.12.004

Edmund, T. H. H., Srinivasan, D., & Liew, A. C. (1998). Short Term Load

Forecsating Using Genetic Algorithm and Neural Networks. IEEE, 576–581.

Fan, S., Chen, L., & Member, S. (2006). Short-Term Load Forecasting Based on

an Adaptive Hybrid Method. IEEE Transaction on Power System, 21(1),

392–401.

Fausett, L. V. (1994). Fundamentals of Neural Networks : Architectures,

Algorithms, and Applications. Prentice-Hall.

G. Lamert-Torres, C.O. Traore, F.G. Mandolesi, & D. Mukhedkar. (1991).

Short-Term Load Forecasting Using A Fuzzy Engineering Tool. IEEE, 36–40.

Hagan, M. T., & Behr, S. M. (1987). The Time Series Approach to Short Term

Load Forecasting. IEEE Transaction on Power System, PWRS-2(3), 785–

791.

Hsu, Y., & Ho, K. (1992). Fuzzy expert systems : an application to short-term

load forecasting. IEEE PROCEEDINGS-C, 139(6), 471–477.

Hsu, Y., & Yang, C. (1991). Design of Artificial Neural Networks for Short-Term Load Forecasting. Part I: Self-Organising Feature Maps for Day Type

Identification. IEEE PROCEEDINGS-C, 138(5), 407–413.

Kim, K., Youn, H., Member, S., & Kang, Y. (2000). Short-Term Load Forecasting for Special Days in Anomalous Load Conditions Using Neural

Networks. IEEE Transaction on Power System, 15(2), 559–565.

Kim, M. S. (2013). Modeling Special-Day Effects for Forecasting Intraday

Electricity Demand. European Journal of Operational Research, 230(1),

170–180. http://doi.org/10.1016/j.ejor.2013.03.039

Li, X., Yang, S., Qi, J., & Yang, S. (2006). Short-Term Electric Load Forecasting

Based on SAPSO-ANN Algorithm. Proceeding of the Fifth International

Conference on Machine Learning and Cybernetics, (August), 13–16.

Lista, M., & Irawan, M. I. (2014). Perbandingan Metode Fuzzy Time Series

Cheng dan Metode Box-Jenkins untuk Memprediksi. JURNAL SAINS DAN

(3)

48

Kartika Ainur Rohmah, 2016

OPTIMAL ANOMALOUS SHORT TERM LOAD FORECASTING BERBASIS ALGORITMA FEED FORWARD BACKPROPAGATION

Universitas Pendidikan Indonesia | repository.upi.edu | perpustakaan.upi.edu

M.M. Tripathi, K. G. U. and S. N. S. (2008). Short-Term Load Forecasting Using

Generalized Regression and Probabilistic Neural. The Electricity Journal.

Metaxiotis, K., Kagiannas, A., Askounis, D., & Psarras, J. (2003). Artificial

Intelligence in Short Term Electric Load Forecasting : A State-of-The-Art

Survey for The Researcher. Energy Conversion and Management, 44, 1525–

1534.

Rahman, S., & Batnagar, R. (1988). An Expert System Based Algorithm for Short

Term Load Forecasting. IEEE Transaction on Power System, 3(2), 392–399.

Sforna, M. (1996). A Neural Network Based Technique for Short-Term

Forecasting of Anomalous Load Perioids. IEEE Transaction on Power

System, 11(4), 1749–1756.

Shrivastava, S., & Pratap, M. (2011). Performance Evaluation of Feed-Forward Neural Network with Soft Computing Techniques for Hand Written English

Alphabets. Applied Soft Computing, 11, 1156–1182.

http://doi.org/10.1016/j.asoc.2010.02.015

Skolthanarat, S., Lewlomphaisarl, U., & Tungpumolrut, K. (2014). Short-term Load Forecasting Algorithm and Optimization in Smart Grid Operations and

Planning. IEEE Conference on Technologies for Sustainability (SusTech),

165–171.

Stefanelli, R. (1991). Two Architechtures Implementing Feed-Forward

Completely Connected Neural Nets. IEEE, 2514–2517.

Wang, X. G., Tang, Z., Tamura, H., Ishii, M., & Sun, W. D. (2004). An Improved Backpropagation Algorithm to Avoid The Local Minima Problem.

Neurocomputing, 56, 455–460. http://doi.org/10.1016/j.neucom.2003.08.006

Wati, S. E., Sebayang, D., & Sitepu, R. (2013). Perbandingan Metode Fuzzy

dengan Regresi Linier Berganda dalam Peramalan Jumlah Produksi. Saintia

Matematika, 1(3), 273–284.

Wibowo, H., Mulyadi, Y., & Abdullah, A. G. (2012). Peramalan Beban Listrik Jangka Pendek Terklasifikasi Berbasis Metode Autoregressive Integrated

Moving Average. Electrans, 11(March), 44–50.

Wuryandari, M. D., & Afrianto, I. (2012). Perbandingan Metode Jaringan Syaraf Tiruan Backpropagation dan Learning Vector Quantization pada Pengenalan

Wajah. Jurnal Komputer Dan Informatika (KOMPUTA), 1, 45–51.

Yuancheng, L., Tingjian, F., Erkeng, Y., & Introduction, I. (2002). Short-term Electrical Load Forecasting Using Least Squares Support Vector Machines.

(4)

49

Kartika Ainur Rohmah, 2016

OPTIMAL ANOMALOUS SHORT TERM LOAD FORECASTING BERBASIS ALGORITMA FEED FORWARD BACKPROPAGATION

Universitas Pendidikan Indonesia | repository.upi.edu | perpustakaan.upi.edu

Zweiri, Y. H., Whidborne, J. F., & Seneviratne, L. D. (2003). A Three-Term

Referensi

Dokumen terkait

The idea behind the use of Feed Forward Neural Network models in load forecasting is simple: it is assumed that future load is dependent on past load and external factors

Kang, “Short-term load forecasting for special days in anomalous load conditions using neural networks and fuzzy inference method,” IEEE Transactions on Power Systems , vol.

SHORT TERM LOAD FORECASTING UNTUK HARI LIBUR PADA KONDISI BEBAN ANOMALI MENGGUNAKAN ALGORITMA HYBRID BACK PROPAGATION-SWARM PARTICLE.. Universitas Pendidikan Indonesia |

Gambar 3.2 Diagram alir (flow chart) desain model multiple regression.. 3.6 Model Algoritma

Unit Commitment Menggunakan Algoritma Simulated Annealing (SA) Pada Pembangkit Belawan Indonesia.. Dynamic Economic Load Dispatch Using Quadratic Programming

“ Modelling and Short Term forecasting of daily peak power demand in Victoria using two dimensional wavelet based SDP models ”.. Electrical Power and

Keyword : Loss of load probability (LOLP), Forced Outage Rate (FOR), Load forecasting, Load Duration Curve, Nuclear Power

Didapat model multi layer feed forward neural network dengan algoritma resilent backpropagation yang akan digunakan untuk meramalkan return harga saham yang terdiri dari