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Four methods for short-term load forecasting using the benefits of artificial intelligence

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Abstract

Four methods are developed for short-term load forecasting and are tested with the actual data from the Turkish Electrical Authority. The method giving the most successful forecasts is a hybrid neural network model which combines off-line and on-line learning and performs real-time forecasts 24-hours in advance. Loads from all day types are predicted with 1.7273% average error for working days, 1.7506% for Saturdays and 2.0605% for Sundays.

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References

  1. Peng TM, Hubele NF, Karady GG (1992) Advancement in the application of neural networks for short–term load forecasting. IEEE Trans Power Syst 7:250–257

    Article  Google Scholar 

  2. Gross G, Galiana FD (1987) Short time load forecasting. Proc IEEE 75:1558–1573

    Google Scholar 

  3. Moghram I, Rahman S (1989) Analysis and evaluation of five short-term load forecasting techniques. IEEE Trans Power Syst 4:1484–1491

    Article  Google Scholar 

  4. Box G, Jenkins G (1976) Time series analysis: forecasting & control, Holden–Day, Orkland, Calif.

    Google Scholar 

  5. Charytoniuk W, Chen MS (2000) Very short-term load forecasting using artificial neural networks. IEEE Trans Power Syst 15:263–268

    Article  Google Scholar 

  6. El-Sharkawi MA, Peng R, Marks RJ (1999) Short term peak load forecast using detrended partitioned data training of a neuro-fuzzy regression machine. Eng Intell Syst Elec 7:197–202

    Google Scholar 

  7. Khotanzad A, Davis MH, Abaye A et al (1996) An artificial neural network hourly temperature forecaster with applications in load forecasting. IEEE Trans Power Syst 11:870–876

    Article  Google Scholar 

  8. Tamimi M, Egbert R (2000) Short term electric load forecasting via fuzzy neural collaboration. Elec Pow Syst Res 56:243–248

    Article  Google Scholar 

  9. Zhang BL, Dong ZY (2001) An adaptive neural-wavelet model for short term load forecasting. Elec Power Syst Res 59:121–129

    Article  Google Scholar 

  10. Khotanzad A, Zhou E, Elragal H (2002) A neuro-fuzzy approach to short-term load forecasting in a price-sensitive environment. IEEE Trans Power Syst 17:1273–1282

    Article  Google Scholar 

  11. Cichocki A, Unbehauen R (1993) Neural networks for optimization and signal processing. John Wiley, Chichester

  12. Balya B (1994) Short term load forecasting using GLVQ algorithm cascaded with a supervised ANN. MSc Thesis, Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey

  13. Pal NR, Bezdek JC, Tsao K (1993) Generalized clustering networks and Kohonen's self-organizing scheme. IEEE Trans Neur Netw 4:549–557

    Article  Google Scholar 

  14. Vemuri V (1988) Artificial neural networks: theoretical concepts. IEEE Computer Society Press, Rockville, Md.

    Google Scholar 

  15. Ozdogan A (1996) Short-term load forecasting using genetically optimized neural network cascaded with a modified Kohonen clustering process. MSc Thesis, Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey

  16. Kohonen T (1990) The self organizing map. Proc IEEE 78:1464–1480

    Article  Google Scholar 

  17. Whitley D, Starkweather T, Bogart C (1990) Genetic algorithms and neural networks: optimizing connections and connectivity. Computing 4:347–361

    Google Scholar 

  18. Maniezzo V (1994) Genetic evolution of the topology and weight distribution of neural networks. IEEE Trans Neur Netw 5:39–53

    Article  Google Scholar 

  19. Tan E (1997) Short-term system load forecasting using fuzzy neural networks. MSc Thesis, Department of Electrical and Electronics Engineering, Middle East Technical University, Ankara, Turkey

  20. Wang LX, Mendel JM (1992) Backpropagation fuzzy system as nonlinear dynamic system identifiers. Proceedings of the IEEE International Conference on Fuzzy Systems. IEEE, Piscataway, N.J., pp 1409–1418

  21. Kosko B (1992) Neural networks and fuzzy systems. Prentice Hall, Englewood Cliffs, N.J.

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Authors and Affiliations

  1. Electrical and Electronics Engineering Department, Middle East Technical University, 06531, Ankara, Turkey

    I. Erkmen

  2. TEON Software & Hardware Design Inc., 10001 Sok No. 26, AOSB Cigli Izmir, Turkey

    A. K. Topalli

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  1. I. Erkmen
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  2. A. K. Topalli
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Correspondence to A. K. Topalli.

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Erkmen, I., Topalli, A.K. Four methods for short-term load forecasting using the benefits of artificial intelligence. Electr Eng 85, 229–233 (2003). https://doi.org/10.1007/s00202-003-0163-9

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  • DOI: https://doi.org/10.1007/s00202-003-0163-9

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