Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 11

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

Electricity peak demand classification with artificial neural networks

, ,

DOI: http://dx.doi.org/10.15439/2017F168

Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 307–315 ()

Full text

Abstract. Demand peaks in electrical power system cause serious challenges for energy providers as these events are typically difficult to foresee and require the grid to support extraordinary consumption levels. Accurate peak forecasting enables utility providers to plan the resources and also to take control actions to balance electricity supply and demand. However, this is difficult in practice as it requires precision in prediction of peaks in advance. In this paper, our contribution is the proposal of data mining scheme to detect the peak load in the electricity system at country level. For this purpose we undertake the approach different from time series forecasting and represent it as pattern recognition problem. We utilize set of artificial neural networks to benefit from accurate detection of the peaks in the Polish power system. The key finding is that the algorithms can accurately detect 96.2\% of the electricity peaks up to 24 hours ahead.

References

  1. M. Goodwin, and A. Yazidi, “A Pattern Recognition Approach for Peak Prediction of Electrical Consumption”. In L. Iliadis, I. Maglogiannis, H. Papadopoulos (Eds) Proc. Artificial Intelligence Applications and Innovations AIAI 2014, IFIP Advances in Information and Communication Technology, vol. 436, Springer, Berlin Heidelberg, 2014, http://dx.doi.org/10.1007/978-3-662-44654-6_26
  2. A. Goia, C. May, and G. Fusai, “Functional clustering and linear regression for peak load forecasting”, International Journal of Forecasting, vol. 26, no. 4, 2010, pp. 700–711, http://dx.doi.org/10.1016/j.ijforecast.2009.05.015
  3. E. Chiodo, and D. Lauria, “Probabilistic description and prediction of electric peak power demand”, Electrical Systems for Aircraft, Railway and Ship Propulsion (ESARS) IEEE, 2012. pp. 1–7, http://dx.doi.org/10.1109/ESARS.2012.6387418
  4. S. Ghosh, “Univariate time-series forecasting of monthly peak demand of electricity in northern India”, International Journal of Indian Culture and Business Management, vol. 1, no. 4, 2008, pp. 466–474, http://dx.doi.org/10.1504/IJICBM.2008.018626
  5. A. A. Mati, B. G. Gajoga, B. Jimoh, A. Adegobye, and D. D. Dajab, “Electricity demand forecasting in Nigeria using time series model”, The Pacific Journal of Science and Technology, vol. 10, no. 2, 2009, pp. 479–485.
  6. C. García-Ascanio, and C. Maté, “Electric power demand forecasting using interval time series: A comparison between VAR and iMLP”, Energy Policy, vol. 38, no. 2, 2010, pp. 715–25, http://dx.doi.org/10.1016/j.enpol.2009.10.007
  7. C. Gibbons, and A. Faruqui, “Quantile Regression for Peak Demand Forecasting”, Available at SSRN 2485657, 2014 Jul 31, http://dx.doi.org/10.2139/ssrn.2485657
  8. J. Nazarko, and W. Zalewski, “The Fuzzy Regression Approach to Peak Load Estimation in Power Distribution Systems”, IEEE Transactions on Power Systems, vol. 14, no. 3, 1999, http://dx.doi.org/10.1109/59.780890
  9. A. J. Al-Shareef, E. A. Mohamed, and E. Al-Judaibi, “Next 24-Hours Load Forecasting using Artificial Neural Network (ANN) for the Western Area of Saudi Arabia”, J. Faculty of Eng. Sci, King Abdulaziz University (KAU), vol.19, no. 2, 2008, pp. 25–40.
  10. G. A., Adepoju, S. O. Ogunjuyigbe, and K. O. Alawode, “Application of Neural Network to Load Forecasting in Nigerian Electrical Power System”, The Pacific Journal of Science and Technology, Akamai University, vol. 8, no. 1, 2007, pp. 68–72.
  11. M. Çunkaş, and A. A. Altun, “Long term electricity demand forecasting in Turkey using artificial neural networks”, Energy Sources, Part B: Economics, Planning, and Policy, vol. 5, no. 3, 2010, pp. 279–289.
  12. L. Ghods, and M. Kalantar, “Long-term peak demand forecasting by using radial basis function neural networks”, Iranian Journal of Electrical and Electronic Engineering, vol. 6, no. 3, 2010, pp. 175–182.
  13. L. Ekonomou, “Greek long-term energy consumption prediction using artificial neural networks”, Energy, vol. 35, no. 2, 2010, pp. 512–517, http://dx.doi.org/10.1016/j.energy.2009.10.018
  14. K. Kandananond, “Forecasting electricity demand in Thailand with an artificial neural network approach”, Energies, vol. 4, no. 8, 2011, pp. 1246–1257, http://dx.doi.org/10.3390/en4081246
  15. P. E. McSharry, S. Bouwman, and G. Bloemhof, “Probabilistic forecasts of the magnitude and timing of peak electricity demand”. IEEE Transactions on Power Systems, vol. 20, no. 2, 2005, pp. 1166–1172, http://dx.doi.org/10.1109/TPWRS.2005.846071
  16. Polish power system dataset, http://www.pse.pl/index.php?dzid=77, accessed 2016/08/12.
  17. S. Rahman, “Formulation and analysis of a rule-based short-term load forecasting algorithm”, Proc. of IEEE, vol. 78, no. 5, 1990, pp. 805–816, http://dx.doi.org/10.1109/5.53400
  18. M. U. Fahad, and N. Arbab, “Factor Affecting Short Term Load Forecasting”, Journal of Clean Energy Technologies, vol. 2, no. 4, 2014, pp. 305–309, http://dx.doi.org/10.7763/JOCET.2014.V2.145
  19. R. J. Hyndman, and Y. Fan, “Sample quantiles in statistical packages”, American Statistician, vol. 50, no. 4, 1996, pp. 361–365, http://dx.doi.org/10.2307/2684934
  20. R Core Team: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, 2015.
  21. K. Gajowniczek, T. Ząbkowski, and R. Szupiluk, “Estimating the ROC curve and its significance for classification models’ assessment”, Quantitative Methods in Economics, vol. 15, no. 2, 2014, pp. 382–391.
  22. K. Gajowniczek, T. Ząbkowski, and A. Orłowski, “Entropy Based Trees to Support Decision Making for Customer Churn Management”, Acta Physica Polonica A, vol. 129, no. 5, 2016, pp. 971–979, http://dx.doi.org/10.12693/APhysPolA.129.971
  23. K. Gajowniczek, K. Karpio, P. Łukasiewicz, A. Orłowski, and T. Ząbkowski, “Q-entropy approach to selecting high income households”, Acta Physica Polonica A, vol. 127, no. 3A, 2015, pp. 38–44, http://dx.doi.org/10.12693/APhysPolA.127.A-38
  24. K. Gajowniczek, T. Ząbkowski, and A. Orłowski, “Comparison of Decision Trees with Renyi and Tsallis Entropy Applied for Imbalanced Churn Dataset”, Annals of Computer Science and Information Systems, vol. 5, 2015, pp. 39–43, http://dx.doi.org/10.15439/2015F121
  25. W. J. Youden, “An index for rating diagnostic tests”, Cancer, vol. 3, 1950, pp. 32–35, http://dx.doi.org/10.1002/1097-0142(1950)3:1%3C32::AID-CNCR2820030106%3E3.0.CO;2-3