Journals / Bilgi Yönetimi / 2020 / Cilt: 3 - Sayı: 2

Electricity Load Forecasting via ANN Approach in Turkish Electricity Markets

Türk Elektrik Piyasalarında YSA Yaklaşımıyla Elektrik Yükü Tahmini

Pages
170–184
Publication Date
2022-10-12
DOI
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AI Summary & Key Takeaways

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

Levenberg-Marquardt (LM) with log-sigmoid transfer function trains the best performance ANN model.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Forecasting electricity load has become the essential task for electric utilities, power plants and regulators.
📊
Key Findings & Data:This article introduces 400 different ANN models for electricity load forecasting.
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Conclusion & Impact:Levenberg-Marquardt (LM) with log-sigmoid transfer function trains the best performance ANN model.
🔬Design:Modeling & Simulation
📍Scope:2014 Verileri
⚙️Variables:Domain Parameters

Abstract

Forecasting electricity load has become the essential task for electric utilities, power plants and regulators. It is essential that electricity load forecasts, which are a vital necessity of energy policies, produce healthy and reliable results. Artificial neural networks (ANN) can learn complex and nonlinear relationships. This article introduces 400 different ANN models for electricity load forecasting. Model performances have compared with Mean Absolute Percentage Error (MAPE) and Diebold-Mariano (DM) test. The electricity load data used for this study range from 2014 to 2016. The variation in forecasting ability of ANN for different models has also discussed. Levenberg-Marquardt (LM) with log-sigmoid transfer function trains the best performance ANN model.

Özet

Elektrik yük tahmini yapabilmek, elektrik hizmetleri, enerji santralleri ve düzenleyiciler için gereklidir. Enerji politikalarını için büyük öneme sahip olan elektrik yükü tahminlerinin sağlıklı ve güvenilir sonuçlar üretmesi esastır. Yapay sinir ağları (YSA) karmaşık ve doğrusal olmayan ilişkileri öğrenebilir. Bu makale, elektrik yükü tahmini için 400 farklı YSA modelini tanımlamaktadır. Model performansları Ortalama Mutlak Yüzde Hata (MAPE) ve Diebold-Mariano (DM) testi ile karşılaştırılmıştır. Bu çalışma için kullanılan elektrik yükü verileri 2014 ile 2016 yılları arasında değişmektedir. Farklı modeller için YSA'nın tahmin kabiliyetleri tartışılmıştır. Log-sigmoid aktarım işlevine sahip Levenberg-Marquardt (LM), en iyi performanslı YSA modelini eğitir.

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Cite This Article

GÖKGÖZ, F., FİLİZ, F. (2022). Electricity Load Forecasting via ANN Approach in Turkish Electricity Markets. Bilgi Yönetimi, Cilt: 3 - Sayı: 2, 170–184. https://ajindex.com/articles/464768

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References (10)

  1. Alfares, H. K. & Nazeeruddin, M. (2002). Electric Load Forecasting: Literature Survey and Classification of Methods. International Journal of Systems Science. 33(1), 23-34.
  2. Ali, S. S., Moinuddin, M., Raza, K. & Adil, S. H. (2014). An Adaptive Learning Rate for Rbfnn Using Time-Domain Feedback Analysis. Scientific World Journal. Doi: 10.1155/2014/850189. Azar, A. T. (2013). Fast Neural Network Learning Algorithms for Medical Applications. Neural Computing and Applications Applications, 23(3-4), 1019-1034.
  3. Basterrech, S., Rubino, G., Mohammed, S. & Soliman, M. (2011). Levenberg - Marquardt Training Algorithms for Random Neural Networks. Computer Journal. 54(1), 125-135.
  4. Buhari, M., & Adamu, S. S. (2012). Short-term Load Forecasting Using Artificial Neural Network. Proceedings of the International Multi-Conference of Engineers and Computer Scientist, 14-16.
  5. Diebold, F. (2007). Elements of Forecasting. Thomson South-western. Fine, T. L. (1999). Feedforward Neural Network Methodology. Springer. Güney, E. S. (2005).
  6. Restructuring, Competition and Requlation in the Turkish Electricity Industry, Tech. Tepav. Günay, M. (2016). Forecasting Annual Gross Electricity Demand by Artificial Neural Networks Using Predicted Values of Socio-Economic Indicators and Climatic Conditions: Case of Turkey. Energy Policy, 90, 92-101.
  7. Hahn, H., Meyer-Nieberg, S. & Pickl, S. (2009). Electric Load Forecasting Methods: Tools for Decision Making. European Journal of Operational Research, 199(3), 902-907.
  8. Haykin, S. S. (1999). Neural Networks: A Comprehensive Foundation. Pearson Prentice Hall. Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001). Neural Networks for Short-Term Load Forecasting: A Review and Evaluation. IEEE Transactions on Power Systems, 16(1), 44-55.
  9. Hobbs, B. F., Jitprapaikulsarn, S., Konda, S., Chankong, V., Loparo, K. A., & Maratukulam, D. J. (1999). Analysis of the Value for Unit Commitment of İmproved Load Forecasts. IEEE Transactions on Power Systems, 14(4), 1342– 1348. Kankal, M., Akpinar, A., Komurcu, M. I. & Ozsahin, T. S. (2011). Modeling and Forecasting of Turkeys Energy Consumption Using Socio-Economic and Demographic Variables. Applied Energy, 88(5), 1927-1939.
  10. Khadse, C. B., Chaudhari, M. A. & Borghate, V. B. (2016). Conjugate Gradient Back- Propagation Based Artificial Neural Network for Real Time Power Quality Assessment. International Journal of Electrical Power & Energy Systems, 82, 197- 206. Kodogiannis, V. S. Amina, M., & I. Petrounias. (2013). A