YAPAY SİNİR AĞLARI İLE TÜRKİYE ELEKTRİK ENERJİSİ TÜKETİMİNİN 2010 YILINA KADAR TAHMİNİ
Yıl 2004,
Cilt: 19 Sayı: 3, 0 - , 10.04.2013
Coşkun Hamzaçebi
Fevzi Kutay
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
Coşkun Hamzaçebi
Fevzi Kutay
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.