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A Flexible Mixed Additive-Multiplicative Model for Load Forecasting in a Smart Grid Setting

  • Eugene A. FeinbergEmail author
  • Jun Fei
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 254)

Abstract

This paper presents a mixed additive-multiplicative model for load forecasting that can be flexibly adapted to accommodate various forecasting needs in a Smart Grid setting. The flexibility of the model allows forecasting the load at different levels: system level, transform substation level, and feeder level. It also enables us to conduct short-term, medium and long-term load forecasting. The model decomposes load into two additive parts. One is independent of weather but dependent on the day of the week (d) and hour of the day (h), denoted as \(L_0(d,h)\). The other is the product of a weather-independent normal load, \(L_1(d,h)\), and weather-dependent factor, f(w). Weather information (w) includes the ambient temperature, relative humidity and their lagged versions. This method has been evaluated on real data for system level, transformer level and feeder level in the Northeastern part of the USA. Unlike many other forecasting methods, this method does not suffer from the accumulation and propagation of errors from prior hours.

Keywords

Load forecasting Additive-multiplicative model Smart grid 

References

  1. 1.
    H.L. Willis, Spatial Electric Load Forecasting (Marcel Dekker, New York, 1996)Google Scholar
  2. 2.
    E.A. Feinberg, D. Genethliou, Load forecasting, in Applied Mathematics for Restructured Electric Power Systems: Optimization, Control, and Computational Intelligence ed. by J.H. Chow, F.F. Wu, J.J. Momoh (Springer, New York, 2005), pp. 269-285Google Scholar
  3. 3.
    A.D.P. Lotufo, C.R. Minussi, Electric power systems load forecasting: a survey, in The IEEE Power Tech Conference, Budapest, Hungary, 1999Google Scholar
  4. 4.
    E. Paparoditis, T. Sapatinas, Short-term load forecasting: the similar shape functional time-series predictor. IEEE Trans. Power syst. 28(4), 3818–3825 (2013)CrossRefGoogle Scholar
  5. 5.
    S. Ruzic, A. Vuckovic, N. Nikolic, Weather sensitive method for short-term load forecasting in electric power utility of Serbia. IEEE Trans. Power Syst. 18, 1581–1586 (2003)CrossRefGoogle Scholar
  6. 6.
    A. Goia, C. May, G. Fusai, Functional clustering and linear regression for peak load forecasting. Int. J. Forecast. 26(4), 700–711 (2010)CrossRefGoogle Scholar
  7. 7.
    N. Abu-Shikhah, F. Elkarmi, O.M. Aloquili, Medium-term electric load forecasting using multivariable linear and non-linear regression. Smart Grid Renew. Energy 2(02), 126 (2011)CrossRefGoogle Scholar
  8. 8.
    Y. Goude, R. Nedellec, N. Kong, Local short and middle term electricity load forecasting with semi-parametric additive models. IEEE Trans. Smart Grid 5, 440–446 (2014)CrossRefGoogle Scholar
  9. 9.
    C.M. Lee, C.N. Ko, Short-term load forecasting using lifting scheme and ARIMA models. Expert Syst. Appl. 38(5), 5902–5911 (2011)CrossRefGoogle Scholar
  10. 10.
    S.S. Pappas, L. Ekonomou, P. Karampelas, D.C. Karamousantas, S.K. Katsikas, G.E. Chatzarakis, P.D. Skafidas, Electricity demand load forecasting of the Hellenic power system using an ARMA model. Electr. Power Syst. Res. 80(3), 256–264 (2010)CrossRefGoogle Scholar
  11. 11.
    Y. Chakhchoukh, P. Panciatici, L. Mili, Electric load forecasting based on statistical robust methods. IEEE Trans. Power Syst. 26(3), 982–991 (2011)CrossRefGoogle Scholar
  12. 12.
    F. Yu, X. Xu, A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network. Appl. Energy 134, 102–113 (2014)CrossRefGoogle Scholar
  13. 13.
    H. Quan, D. Srinivasan, A. Khosravi, Short-term load and wind power forecasting using neural network-based prediction intervals. IEEE Trans. Neural Netw. Learn. Syst. 25(2), 303–315 (2014)CrossRefGoogle Scholar
  14. 14.
    C.N. Ko, C.M. Lee, Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter. Energy 49, 413–422 (2013)CrossRefGoogle Scholar
  15. 15.
    D. Niu, Y. Wang, D.D. Wu, Power load forecasting using support vector machine and ant colony optimization. Expert Syst. Appl. 37(3), 2531–2539 (2010)CrossRefGoogle Scholar
  16. 16.
    W.C. Hong, Application of chaotic ant swarm optimization in electric load forecasting. Energy Policy 38(10), 5830–5839 (2010)CrossRefGoogle Scholar
  17. 17.
    A. Khosravi, S. Nahavandi, D. Creighton, D. Srinivasan, Interval type-2 fuzzy logic systems for load forecasting: a comparative study. IEEE Trans. Power Syst. 27(3), 1274–1282 (2012)CrossRefGoogle Scholar
  18. 18.
    V.H. Hinojosa, A. Hoese, Short-term load forecasting using fuzzy inductive reasoning and evolutionary algorithms. IEEE Trans. Power Syst. 25(1), 565–574 (2010)CrossRefGoogle Scholar
  19. 19.
    H. Hagras, C. Wagner, Towards the wide spread use of type-2 fuzzy logic systems in real world applications. IEEE Comput. Intell. Mag. 7(3), 14–24 (2012)CrossRefGoogle Scholar
  20. 20.
    E.E. Elattar, J. Goulermas, Q.H. Wu, Electric load forecasting based on locally weighted support vector regression. IEEE Trans. Syst. Man Cybern. Part C (Applications and Reviews) 40(4), 438–447 (2010)CrossRefGoogle Scholar
  21. 21.
    E. Ceperic, V. Ceperic, A. Baric, A strategy for short-term load forecasting by support vector regression machines. IEEE Trans. Power Syst. 28(4), 4356–4364 (2013)CrossRefGoogle Scholar
  22. 22.
    E.A. Feinberg, J. Fei, J.T. Hajagos, R.R. Rossin, Smart grid software applications for distribution network load forecasting, in Proceedings of the First International Conference on Smart Grids, Green Communications and IT Energy-Aware Technologies, Venice, 22–27 May 2011Google Scholar
  23. 23.
    E.A. Feinberg, D. Genethliou, J.T. Hajagos, B.G. Irrgang, R.R. Rossin, Load pocket forecasting software, in Proceedings of 2004 IEEE Power Systems Conference & Exposition, New York, 10–13 October 2004Google Scholar
  24. 24.
    E.A. Feinberg, J. Hu, E. Yuan, A stochastic search algorithm for voltage and reactive power control with switching costs and ZIP load model. Electr. Power Syst. Res. 133, 328–337 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Applied Mathematics & Statistics and Advanced Energy CenterStony Brook UniversityStony BrookUSA

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