BibTex RIS Kaynak Göster

YAPAY SİNİR AĞLARI İLE TÜRKİYE ELEKTRİK ENERJİSİ TÜKETİMİNİN 2010 YILINA KADAR TAHMİNİ

Yıl 2004, Cilt: 19 Sayı: 3, 0 - , 10.04.2013

Öz

Bu çalışmada, uzun dönemli elektrik enerjisi tüketimi tahmininde yapay sinir ağlarının kullanılması araştırılmıştır. Yapay sinir ağları teknikleri ile bulunan sonuçlar, Box-Jenkins modelleri ve regresyon tekniği ile karşılaştırılmıştır. Bulunan sonuçlar yapay sinir ağlarının elektrik enerjisi tüketiminde iyi bir tahmin aracı olduğunu göstermiştir.

Kaynakça

  • Hill, T., O’Connor, M., Remus, W., “Neural Networks Models for Time Series Forecasts”, Management Sciences, Cilt 42, No 7,1082-1092, 1996.
  • Sharda, R., Patil, R.B., “Connectionist Approach to Time Series Prediction: An Emprical Test”, Journal of Intelligent Manufacturing, Cilt 3, 317-323, 1992.
  • Tang, Z., Almeida, C., Fishwick, P.A., “Time Series Forecasting Using Neural Networks vs Box-Jenkins Methodology”, Simulation, Cilt 57, No 5, 303-310, 1991.
  • Zhang, G., Patuwo, B.E., Hu, M.Y., “Forecasting with Artificial Neural Networks: The State of the Art”, Inter. Journal of Forecasting, Cilt 14, 35-62, 1998.
  • Park, J., Sandberg, I.W., “Universal Approximation Using Radial Basis Function Networks”, Neural Computation, Cilt 3, 246-257, 1991.
  • Peng, T.M., Hubele, N.F., Karady, G.G., “Advancement in the Application of Neural Networks for STLF”, IEEE Trans. on Power Sys., Cilt 7, No1, 250-257, 1992.
  • Liang, R.H., Cheng, C.C., “Short-Term Load Forecasting by a Neuro-Fuzzy Based Approach”, Electrical Power and Energy Systems, Cilt 24, 103-111, 2002.
  • Tamimi, M., Egbert, R., “Short Term Electric Load Forecasting Via Fuzzy Neural Collaboration”, Electric Power Systems Research, Cilt 56, 243-248, 2000.
  • Dash, P.K., Satpathy, H.P., Liew, A.C., “A Real-Time Short-Term Peak and Average Load Forecasting System Using a Self-Organising Fuzzy Neural Network”, Engineering Applications of Artificial Intelligence, Cilt 11, No 2, 307-316, 1998.
  • Srinivisan, D., “Evolving Artificial Neural Networks for Short Term Load Forecasting”, Neurocomputing, Cilt 23, 265-276, 1998.
  • Kodogiannis, V.S., Anagnostakis, E.M., “A Study of Advanced Learning Algorithms for STLF”, Eng. App. of Artificial Intelligence, Cilt 12, No 2, 159-173, 1999.
  • Kermanshahi, B., Iwamiya, H., “Up to Year 2020 Load Forecasting Using Neural Nets”, Electrical Power and Energy Systems, Cilt 24, 789-797, 2002.
  • Al-Saba, T., El-Amin, I., “Artificial Neural Networks as Applied to Long-Term Demand Forecasting”, Artificial Intelligence in Engineering, Cilt 13, 189-197, 1999.
  • Parlos, A.G., Oufi, E., Muthusami, J., Patton A.D., Atiya A.F., “Development of an Intelligent Long-Term Electrical Load Forecasting System”, Intelligent System Applications to Power System Proceedings, 288-292, 1996.
  • Padmakumari, K., Mohandas, K.P., Thiruvengadam, S., “Long Term Distribution Demand Fore. Using Neuro Fuzzy Computations”, In. Jo. of Electrical Power and Energy Systems, Cilt 21, No 5, 315-322, 1999.
  • Box, G., Jenkins, M., Time Series Analaysis Forecasting and Control, Holden Day Inc., California, 1976.
  • Kaastra, I., Boyd, M., “Designing a Neural Network for Forecasting Financial and Economic Time Series”, Neurocomputing, Cilt 10, 215-236, 1996.
  • Haykin, S., Neural Networks: A Comprehensive Foundation, Perenctice Hall, New Jersey, 1999.
  • Werbos, P.J., Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, PhD thesis, Harvard University, 1974.
  • Rumelhart, D.E., Hinton, G.E., Williams, R.J., “Learning Internal Represantation by Back-Propagating Errors”, In: Rumelhart D.E., McCleland J.L., The PDP Research Group, Paralel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, MA, 1986.
  • Kartalopoulos, S.V., Understanding Neural Network and Fuzzy Logic, IEEE Press, New York,1996.
  • Demuth, H., Beale, M., Neural Network Toolbox For Use With MATLAB User’s Guide Version 4, MA, 2000.
  • Altaş, M., Özkan, H.F., Çelebi E., 2002 “Enerji İstatistikleri”, Türkiye 9. Enerji Kongresi, 220, İstanbul, 2002.
  • Devlet İstatistik Enstitüsü, Elektrik, Gaz ve Su İstatistikleri, 4, 19.
  • Faraway, J., Chatfield, C., “Time Series Forecasting With Neural Networks: A Comparative Study Using The Airline Data”, Appl. Statist., Cilt 47, 231-250, 1998.
Yıl 2004, Cilt: 19 Sayı: 3, 0 - , 10.04.2013

Öz

Kaynakça

  • Hill, T., O’Connor, M., Remus, W., “Neural Networks Models for Time Series Forecasts”, Management Sciences, Cilt 42, No 7,1082-1092, 1996.
  • Sharda, R., Patil, R.B., “Connectionist Approach to Time Series Prediction: An Emprical Test”, Journal of Intelligent Manufacturing, Cilt 3, 317-323, 1992.
  • Tang, Z., Almeida, C., Fishwick, P.A., “Time Series Forecasting Using Neural Networks vs Box-Jenkins Methodology”, Simulation, Cilt 57, No 5, 303-310, 1991.
  • Zhang, G., Patuwo, B.E., Hu, M.Y., “Forecasting with Artificial Neural Networks: The State of the Art”, Inter. Journal of Forecasting, Cilt 14, 35-62, 1998.
  • Park, J., Sandberg, I.W., “Universal Approximation Using Radial Basis Function Networks”, Neural Computation, Cilt 3, 246-257, 1991.
  • Peng, T.M., Hubele, N.F., Karady, G.G., “Advancement in the Application of Neural Networks for STLF”, IEEE Trans. on Power Sys., Cilt 7, No1, 250-257, 1992.
  • Liang, R.H., Cheng, C.C., “Short-Term Load Forecasting by a Neuro-Fuzzy Based Approach”, Electrical Power and Energy Systems, Cilt 24, 103-111, 2002.
  • Tamimi, M., Egbert, R., “Short Term Electric Load Forecasting Via Fuzzy Neural Collaboration”, Electric Power Systems Research, Cilt 56, 243-248, 2000.
  • Dash, P.K., Satpathy, H.P., Liew, A.C., “A Real-Time Short-Term Peak and Average Load Forecasting System Using a Self-Organising Fuzzy Neural Network”, Engineering Applications of Artificial Intelligence, Cilt 11, No 2, 307-316, 1998.
  • Srinivisan, D., “Evolving Artificial Neural Networks for Short Term Load Forecasting”, Neurocomputing, Cilt 23, 265-276, 1998.
  • Kodogiannis, V.S., Anagnostakis, E.M., “A Study of Advanced Learning Algorithms for STLF”, Eng. App. of Artificial Intelligence, Cilt 12, No 2, 159-173, 1999.
  • Kermanshahi, B., Iwamiya, H., “Up to Year 2020 Load Forecasting Using Neural Nets”, Electrical Power and Energy Systems, Cilt 24, 789-797, 2002.
  • Al-Saba, T., El-Amin, I., “Artificial Neural Networks as Applied to Long-Term Demand Forecasting”, Artificial Intelligence in Engineering, Cilt 13, 189-197, 1999.
  • Parlos, A.G., Oufi, E., Muthusami, J., Patton A.D., Atiya A.F., “Development of an Intelligent Long-Term Electrical Load Forecasting System”, Intelligent System Applications to Power System Proceedings, 288-292, 1996.
  • Padmakumari, K., Mohandas, K.P., Thiruvengadam, S., “Long Term Distribution Demand Fore. Using Neuro Fuzzy Computations”, In. Jo. of Electrical Power and Energy Systems, Cilt 21, No 5, 315-322, 1999.
  • Box, G., Jenkins, M., Time Series Analaysis Forecasting and Control, Holden Day Inc., California, 1976.
  • Kaastra, I., Boyd, M., “Designing a Neural Network for Forecasting Financial and Economic Time Series”, Neurocomputing, Cilt 10, 215-236, 1996.
  • Haykin, S., Neural Networks: A Comprehensive Foundation, Perenctice Hall, New Jersey, 1999.
  • Werbos, P.J., Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences, PhD thesis, Harvard University, 1974.
  • Rumelhart, D.E., Hinton, G.E., Williams, R.J., “Learning Internal Represantation by Back-Propagating Errors”, In: Rumelhart D.E., McCleland J.L., The PDP Research Group, Paralel Distributed Processing: Explorations in the Microstructure of Cognition, MIT Press, MA, 1986.
  • Kartalopoulos, S.V., Understanding Neural Network and Fuzzy Logic, IEEE Press, New York,1996.
  • Demuth, H., Beale, M., Neural Network Toolbox For Use With MATLAB User’s Guide Version 4, MA, 2000.
  • Altaş, M., Özkan, H.F., Çelebi E., 2002 “Enerji İstatistikleri”, Türkiye 9. Enerji Kongresi, 220, İstanbul, 2002.
  • Devlet İstatistik Enstitüsü, Elektrik, Gaz ve Su İstatistikleri, 4, 19.
  • Faraway, J., Chatfield, C., “Time Series Forecasting With Neural Networks: A Comparative Study Using The Airline Data”, Appl. Statist., Cilt 47, 231-250, 1998.
Toplam 25 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Coşkun Hamzaçebi Bu kişi benim

Fevzi Kutay Bu kişi benim

Yayımlanma Tarihi 10 Nisan 2013
Gönderilme Tarihi 10 Nisan 2013
Yayımlandığı Sayı Yıl 2004 Cilt: 19 Sayı: 3

Kaynak Göster

APA Hamzaçebi, C., & Kutay, F. (2013). YAPAY SİNİR AĞLARI İLE TÜRKİYE ELEKTRİK ENERJİSİ TÜKETİMİNİN 2010 YILINA KADAR TAHMİNİ. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 19(3).
AMA Hamzaçebi C, Kutay F. YAPAY SİNİR AĞLARI İLE TÜRKİYE ELEKTRİK ENERJİSİ TÜKETİMİNİN 2010 YILINA KADAR TAHMİNİ. GUMMFD. Mart 2013;19(3).
Chicago Hamzaçebi, Coşkun, ve Fevzi Kutay. “YAPAY SİNİR AĞLARI İLE TÜRKİYE ELEKTRİK ENERJİSİ TÜKETİMİNİN 2010 YILINA KADAR TAHMİNİ”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 19, sy. 3 (Mart 2013).
EndNote Hamzaçebi C, Kutay F (01 Mart 2013) YAPAY SİNİR AĞLARI İLE TÜRKİYE ELEKTRİK ENERJİSİ TÜKETİMİNİN 2010 YILINA KADAR TAHMİNİ. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 19 3
IEEE C. Hamzaçebi ve F. Kutay, “YAPAY SİNİR AĞLARI İLE TÜRKİYE ELEKTRİK ENERJİSİ TÜKETİMİNİN 2010 YILINA KADAR TAHMİNİ”, GUMMFD, c. 19, sy. 3, 2013.
ISNAD Hamzaçebi, Coşkun - Kutay, Fevzi. “YAPAY SİNİR AĞLARI İLE TÜRKİYE ELEKTRİK ENERJİSİ TÜKETİMİNİN 2010 YILINA KADAR TAHMİNİ”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 19/3 (Mart 2013).
JAMA Hamzaçebi C, Kutay F. YAPAY SİNİR AĞLARI İLE TÜRKİYE ELEKTRİK ENERJİSİ TÜKETİMİNİN 2010 YILINA KADAR TAHMİNİ. GUMMFD. 2013;19.
MLA Hamzaçebi, Coşkun ve Fevzi Kutay. “YAPAY SİNİR AĞLARI İLE TÜRKİYE ELEKTRİK ENERJİSİ TÜKETİMİNİN 2010 YILINA KADAR TAHMİNİ”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 19, sy. 3, 2013.
Vancouver Hamzaçebi C, Kutay F. YAPAY SİNİR AĞLARI İLE TÜRKİYE ELEKTRİK ENERJİSİ TÜKETİMİNİN 2010 YILINA KADAR TAHMİNİ. GUMMFD. 2013;19(3).