Skip to main content

Modeling for Energy Demand Forecasting

  • Chapter
  • First Online:
Intelligent Energy Demand Forecasting

Part of the book series: Lecture Notes in Energy ((LNEN,volume 10))

Abstract

As mentioned in Chap. 1, the electric load forecasting methods can be classified in three categories [1–12]:

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Moghram I, Rahman S (1989) Analysis and evaluation of five short-term load forecasting techniques. IEEE Trans Power Syst 4:1484–1491. doi:10.1109/59.41700

    Article  Google Scholar 

  2. El-Hawary ME, Mbamalu GAN (1990) Short-term power system load forecasting using the iteratively reweighted least squares algorithm. Electric Power Syst Res 19:11–22. doi:10.1016/0378-7796(90)90003-L

    Article  Google Scholar 

  3. Papalexopoulos AD, Hesterberg TC (1990) A regression-based approach to short-term system load forecasting. IEEE Trans Power Syst 5:1535–1547. doi:10.1109/59.99410

    Article  Google Scholar 

  4. Grady WM, Groce LA, Huebner TM, Lu QC, Crawford MM (1991) Enhancement, implementation and performance of an adaptive short-term load forecasting algorithm. IEEE Trans Power Syst 6:1404–1410. doi:10.1109/59.116982

    Article  Google Scholar 

  5. Al-Fuhaid AS, EL-Sayed MA, Mahmoud MS (1997) Cascaded artificial neural networks for short-term load forecasting. IEEE Trans Power Syst 12:1524–1529. doi:10.1109/59.627852

    Article  Google Scholar 

  6. Hong WC (2009) Hybrid evolutionary algorithms in a SVR-based electric load forecasting model. Int J Electr Power Energ Syst 31:409–417. doi:10.1016/j.ijepes.2009.03.020

    Article  Google Scholar 

  7. Hong WC (2009) Chaotic particle swarm optimization algorithm in a support vector regression electric load forecasting model. Energy Convers Manage 50:105–117. doi:10.1016/j.enconman.2008.08.031

    Article  Google Scholar 

  8. Hong WC (2009) Electric load forecasting by support vector model. Appl Math Model 33:2444–2454. doi:10.1016/j.apm.2008.07.010

    Article  MATH  Google Scholar 

  9. Hong WC (2010) Application of chaotic ant swarm optimization in electric load forecasting. Energ Policy 38:5830–5839. doi:10.1016/j.enpol.2010.05.033

    Article  Google Scholar 

  10. Pai PF, Hong WC (2005) Forecasting regional electric load based on recurrent support vector machines with genetic algorithms. Electric Power Syst Res 74:417–425. doi:10.1016/j.epsr.2005.01.006

    Article  Google Scholar 

  11. Pai PF, Hong WC (2005) Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Convers Manage 46:2669–2688. doi:10.1016/j.enconman.2005.02.004

    Article  Google Scholar 

  12. Hong WC, Dong Y, Zhang WY, Chen LY, Panigrahi BK (2013) Cyclic electric load forecasting by seasonal SVR with chaotic genetic algorithm. Int J Electr Power Energ Syst 44:604–614. doi:10.1016/j.ijepes.2012.08.010

    Article  Google Scholar 

  13. Box GEP, Jenkins GM (1970) Time series analysis, forecasting and control. Holden-Day, San Francisco, CA

    MATH  Google Scholar 

  14. Abu-El-Magd MA, Sinha NK (1982) Short-term load demand modeling and forecasting: a review. IEEE Trans Syst Man Cybern 12:370–382. doi:10.1109/TSMC.1982.4308827

    Article  Google Scholar 

  15. Soliman SA, Persaud S, El-Nagar K, El-Hawary ME (1997) Application of least absolute value parameter estimation based on linear programming to short-term load forecasting. Int J Electr Power Energ Syst 19:209–216. doi:10.1016/S0142-0615(96)00048-8

    Article  Google Scholar 

  16. Holt CC (1957) Forecasting seasonal and trends by exponentially weighted averages. Carnegie Institute of Technology, Pittsburgh, PA

    Google Scholar 

  17. Winters PR (1960) Forecasting sales by exponentially weighted moving averages. Manag Sci 6:324–342. doi:10.1287/mnsc.6.3.324

    Article  MathSciNet  MATH  Google Scholar 

  18. Specht DA (1991) A general regression neural network. IEEE Trans Neural Netw 2:568–576. doi:10.1109/72.97934

    Article  Google Scholar 

  19. Cao L, Gu Q (2002) Dynamic support vector machines for non-stationary time series forecasting. Intell Data Anal 6:67–83

    MATH  Google Scholar 

  20. Vapnik V (1995) The nature of statistical learning theory. Springer, New York, NY

    MATH  Google Scholar 

  21. Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20:273–297. doi:10.1023/A:1022627411411

    MATH  Google Scholar 

  22. Haykin S (1999) Neural networks: a comprehensive foundation. Prentice-Hall, Upper Saddle River, NJ

    MATH  Google Scholar 

  23. Shawe-Taylor J, Bartlett PL, Williamson RC, Anthony M (1998) Structural risk minimization over data-dependent hierarchies. IEEE Trans Inf Theory 44:1926–1940. doi:10.1109/18.705570

    Article  MathSciNet  MATH  Google Scholar 

  24. Amari S, Wu S (1999) Improving support vector machine classifiers by modifying kernel functions. Neural Netw 12:783–789. doi:10.1016/S0893-6080(99)00032-5

    Article  Google Scholar 

  25. Vojislav K (2001) Learning and soft computing—support vector machines, neural networks and fuzzy logic models. The MIT Press, Cambridge, MA, 2001

    MATH  Google Scholar 

  26. Smola AJ, Schölkopf B, Müller KR (1998) The connection between regularization operators and support vector kernels. Neural Netw 11:637–649. doi:10.1016/S0893-6080(98)00032-X

    Article  Google Scholar 

  27. Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 17:113–126. doi:10.1016/S0893-6080(03)00169-2

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag London

About this chapter

Cite this chapter

Hong, WC. (2013). Modeling for Energy Demand Forecasting. In: Intelligent Energy Demand Forecasting. Lecture Notes in Energy, vol 10. Springer, London. https://doi.org/10.1007/978-1-4471-4968-2_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-4968-2_2

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4967-5

  • Online ISBN: 978-1-4471-4968-2

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics