S TE 1100243 Bibliograpy
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 ShortTerm Load Forecasting of Anomalous Days. IEEE Porto Power Tech
Conference.
Choi, B., Lee, J., & Kim, D. (2008). Solving Local Minima Problem with Large
Number of Hidden Nodes on Two-Layered Feed-Forward Artificial Neural
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
47
Networks.
Neurocomputing,
http://doi.org/10.1016/j.neucom.2008.04.004
71,
3640–3643.
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). ShortTerm 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
SENI POMITS, 3(2), A–34–A–39.
Kartika Ainur Rohmah, 2016
OPTIMAL ANOMALOUS SHORT TERM LOAD FORECASTING BERBASIS ALGORITMA FEED
FORWARD BACKPROPAGATION
Universitas Pendidikan Indonesia | repository.upi.edu | perpustakaan.upi.edu
48
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
Completely Connected Neural Nets. IEEE, 2514–2517.
Feed-Forward
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.
IEEE, 230–233.
Kartika Ainur Rohmah, 2016
OPTIMAL ANOMALOUS SHORT TERM LOAD FORECASTING BERBASIS ALGORITMA FEED
FORWARD BACKPROPAGATION
Universitas Pendidikan Indonesia | repository.upi.edu | perpustakaan.upi.edu
49
Zweiri, Y. H., Whidborne, J. F., & Seneviratne, L. D. (2003). A Three-Term
Backpropagation Algorithm. Neurocomputing, 50, 305–318.
Kartika Ainur Rohmah, 2016
OPTIMAL ANOMALOUS SHORT TERM LOAD FORECASTING BERBASIS ALGORITMA FEED
FORWARD BACKPROPAGATION
Universitas Pendidikan Indonesia | repository.upi.edu | perpustakaan.upi.edu
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 ShortTerm Load Forecasting of Anomalous Days. IEEE Porto Power Tech
Conference.
Choi, B., Lee, J., & Kim, D. (2008). Solving Local Minima Problem with Large
Number of Hidden Nodes on Two-Layered Feed-Forward Artificial Neural
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
47
Networks.
Neurocomputing,
http://doi.org/10.1016/j.neucom.2008.04.004
71,
3640–3643.
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). ShortTerm 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
SENI POMITS, 3(2), A–34–A–39.
Kartika Ainur Rohmah, 2016
OPTIMAL ANOMALOUS SHORT TERM LOAD FORECASTING BERBASIS ALGORITMA FEED
FORWARD BACKPROPAGATION
Universitas Pendidikan Indonesia | repository.upi.edu | perpustakaan.upi.edu
48
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
Completely Connected Neural Nets. IEEE, 2514–2517.
Feed-Forward
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.
IEEE, 230–233.
Kartika Ainur Rohmah, 2016
OPTIMAL ANOMALOUS SHORT TERM LOAD FORECASTING BERBASIS ALGORITMA FEED
FORWARD BACKPROPAGATION
Universitas Pendidikan Indonesia | repository.upi.edu | perpustakaan.upi.edu
49
Zweiri, Y. H., Whidborne, J. F., & Seneviratne, L. D. (2003). A Three-Term
Backpropagation Algorithm. Neurocomputing, 50, 305–318.
Kartika Ainur Rohmah, 2016
OPTIMAL ANOMALOUS SHORT TERM LOAD FORECASTING BERBASIS ALGORITMA FEED
FORWARD BACKPROPAGATION
Universitas Pendidikan Indonesia | repository.upi.edu | perpustakaan.upi.edu