Development of a Novel Approach for Electricity Forecasting

Chapter
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 247)

Abstract

In this chapter an innovative method for one and seven-day forecast of electricity load is proposed. The new approach has been tested on three different cases from south-west Western Australia’s interconnected system. They have been tested under the most realistic conditions by considering only minimum and maximum forecasts of temperature and relative humidity as available future inputs. Two different nonlinear approaches of neural networks and decision trees have been applied to fit proper models. A modified version of mean absolute percentage error (MMAPE) of each model over the test year is presented. By applying a developed criterion to recognize the dominant component of the electricity load, user of this work will be able to choose the most efficient forecasting method.

Keywords

Commercial load Decision trees Industrial load Load forecasting Neural networks Residential load Signal reconstruction 

References

  1. 1.
    Weron R (2006) Modeling and forecasting electricity loads and prices: a statistical approach. Wiley, London, p 178CrossRefGoogle Scholar
  2. 2.
    Temraz HK, Salama MMA, Chikhani AY (1997) Review of electric load forecasting methods. In: IEEE Canadian conference on electrical and computer engineering, vol 1. pp 289–292Google Scholar
  3. 3.
    Kourtis G, Hadjipaschalis I, Poullikkas A (2011) An overview of load demand and price forecasting methodologies. Int J Energy Environ 2(1):123–150Google Scholar
  4. 4.
    Hahn H, Meyer-Nieberg S, Pickl S (2009) Electric load forecasting methods: tools for decision making. Eur J Oper Res 199(3):902–907CrossRefMATHGoogle Scholar
  5. 5.
    Sharaf A, Lie T, Gooi H (1993) A neural network based short term load forecasting model. In: IEEE Canadian conference on electrical and computer engineering, pp 325–328Google Scholar
  6. 6.
    Park DC, El-Sharkawi M, Marks R, Atlas L, Damborg M (1991) Electric load forecasting using an artificial neural network. IEEE Trans Power Syst 6(2):442–449CrossRefGoogle Scholar
  7. 7.
    Amral N, King D, Ozveren CS (2008) Application of artificial neural network for short term load forecasting. In: 43rd international universities power engineering conference, pp 1–5Google Scholar
  8. 8.
    Munkhjargal S, Manusov VZ (2004) Artificial neural network based short-term load forecasting. In: The 8th Russian-Korean international symposium on science and technology, vol 1. pp 262–264Google Scholar
  9. 9.
    Peng T, Hubele N, Karady G (1993) An adaptive neural network approach to one-week ahead load forecasting. IEEE Trans Power Syst 8(3):1195–1203CrossRefGoogle Scholar
  10. 10.
    Bala PK (2010) Decision tree based demand forecasts for improving inventory performance. In: IEEE international conference on industrial engineering and engineering management, pp 1926–1930Google Scholar
  11. 11.
    Lobato E, Ugedo A, Rouco R (2006) Decision trees applied to spanish power systems applications. In: IEEE 9th international conference on probabilistic methods applied to power systems, pp 1–6Google Scholar
  12. 12.
    Willis HL (2002) Spatial electric load forecasting, vol 71(2), 2nd edn. CRC Press, Boca Raton, p 760Google Scholar
  13. 13.
    Moghaddam M, Bahri P (2012) A novel approach for forecasting of residential, commercial and industrial electricity loads. In: Lecture notes in engineering and computer science: proceedings of the world congress on engineering and computer science, WCECS, vol. 2. pp 1365–1371Google Scholar
  14. 14.
    Asber D, Lefebvre S, Asber J, Saad M, Desbiens C (2007) Non-parametric short-term load forecasting. Int J Electr Power Energy Syst 29(8):630–635CrossRefGoogle Scholar
  15. 15.
    Seppälä A (1996) Load research and load estimation in electricity distribution. Technical research centre of FinlandGoogle Scholar
  16. 16.
    Hyndman RJ, Fan S (2010) Density Forecasting for Long-Term Peak Electricity Demand. IEEE Trans Power Syst 25(2):1142–1153CrossRefGoogle Scholar
  17. 17.
    Chicco G, Napoli R, Piglione F (2001) Load pattern clustering for short-term load forecasting of anomalous days. In: IEEE porto power tech proceedings, vol 2Google Scholar
  18. 18.
    Jain A, Satish B (2009) Clustering based short term load forecasting using artificial neural network. In: IEEE/PES power systems conference and exposition, pp 1–7Google Scholar
  19. 19.
    Meng M, Chang Lu J, Sun W (2006) Short-term load forecasting based on ant colony clustering and improved BP neural networks. In: International conference on machine learning and cybernetics, pp 3012–3015Google Scholar
  20. 20.
    Dietterich T (2000) An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn 22:1–22Google Scholar
  21. 21.
    Ranaweera DK, Karady GG, Farmer RG (1997) Economic impact analysis of load forecasting. IEEE Trans Power Syst 12(3):1388–1392Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.School of Engineering and Information TechnologyMurdoch UniversityPerthAustralia

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