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Journals / Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi / 2004 / Cilt: 19 - Sayı: 3

Electric consumption forecasting of Turkey using artificial neural networks up to year 2010

Yapay sinir ağları ile Türkiye elektrik enerjisi tüketiminin 2010 yılına kadar tahmini

Fevzi KUTAY
Coşkun HAMZAÇEBİ
Pages
227–233
Publication Date
2022-10-18
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

It is shown that, artificial neural network is a good forecasting tool for forecasting of electric energy consumption.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study is about using of artificial neural networks for long-term electric energy consumption forecasting.
📊
Key Findings & Data:The results obtained by artificial neural networks are compared to those obtained by Box-Jenkins models and regression technique.
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Conclusion & Impact:It is shown that, artificial neural network is a good forecasting tool for forecasting of electric energy consumption.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Domain Parameters

Abstract

This study is about using of artificial neural networks for long-term electric energy consumption forecasting. The results obtained by artificial neural networks are compared to those obtained by Box-Jenkins models and regression technique. It is shown that, artificial neural network is a good forecasting tool for forecasting of electric energy consumption.

Özet

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.

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Cite This Article

KUTAY, F., HAMZAÇEBİ, C. (2022). Electric consumption forecasting of Turkey using artificial neural networks up to year 2010. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Cilt: 19 - Sayı: 3, 227–233. https://ajindex.com/articles/524263

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References (1)

  1. Hill, T., O'Connor, M., Remus, W., "Neural Networks Models for Time Series Forecasts",Management Sciences, Cilt 42, No 7,1082-1092, 1996. 2.Sharda, R., Patil, R.B., "Connectionist Approach to Time Series Prediction: An Emprical Test", Journal of Intelligent Manufacturing, Cilt 3, 317-323,1992. 3.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. 4.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. 5.Park, J., Sandberg, I.W.,"Universal Approximation Using Radial Basis Function Networks", Neural Computation, Cilt 3, 246-257,1991. 6.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, Nol, 250-257,1992. 7.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. 8.Tamimi, M., Egbert, R., "Short Term Electric Load Forecasting Via Fuzzy Neural Collaboration", Electric Power Systems Research, Cilt 56, 243-248, 2000. 9.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. 10.Srinivisan, D., "Evolving Artificial Neural Networks for Short Term Load Forecasting",Neurocomputing, Cilt 23, 265-276,1998. 11.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. 12.Kermanshahi, B., Iwamiya, H., "Up to Year 2020 Load Forecasting Using Neural Nets", Electrical Power and Energy Systems, Cilt 24, 789-797,2002. 13.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. 14.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. 15.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. 16.Box, G., Jenkins, M., Time Series