Efficient Load Scheduling Algorithm Using Artificial Neural Network in an Isolated Power System

  • Vijo M. JoyEmail author
  • S. Krishnakumar
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 98)


In this paper, an efficient load scheduling technique is presented to meet the unpredictable power supply requirements. The power consumption in upcoming days’ must be scheduled in a power system. The accuracy of the system significantly affects the economic operation and consistency of the system. The power generation system fails due to instability at the peak load time. Usually load shedding procedure is used to compensate demanded load. Unnecessary and extra loads are disconnected in load shedding. The proposed system overcomes this difficult by forecast the load based on the load affected constraints. To predict and schedule the load with the previous data is a challenging process when an unexpected change occurs - like days with extreme weather or special days. With the current advance of artificially intelligent tools, it is potentially possible to improve the existing demand of load. For optimal load scheduling, Artificial neural networks are used. The Levenberg-Marquardt backpropagation algorithm is used for the training purpose to minimize the error function. The results are compared by correlation analysis.


Load scheduling Artificial neural network Backpropagation The regression method 


  1. 1.
    Hooshmand, R., Moazzami, M.: Optimum design of adaptive under frequency load shedding using artificial neural networks in isolated power system. Electr. Power Energy Syst. 42, 220–228 (2012)Google Scholar
  2. 2.
    Mathur, D.: Maximum power point tracking with artificial neural network. Int. J. Emerg. Sci. Eng. 2(3), 38–42 (2014). ISSN 2319-6378Google Scholar
  3. 3.
    Kumar, S.S., Neha, S.: Load scheduling algorithm prediction for multiple tasks using time series neural network. IJARCSSE 3(5), 554–558 (2013). ISSN 2277 128XGoogle Scholar
  4. 4.
    Kumaran Kumar, J., Ravi, G.: ANN-based long term sector-wise electrical energy forecasting. ARPN J. Eng. Appl. Sci. 10(1), 115–121 (2015). ISSN 1819-6608Google Scholar
  5. 5.
    Hsu, C.T., Kang, M.S., Chen, C.S.: Design of adaptive load shedding by artificial neural networks. IEE Pro.-Gener. Transm. Distrib. 152(3), 415–421 (2005)Google Scholar
  6. 6.
    Gonzalez, P.A., Zamarreno, J.M.: Prediction of hourly energy consumption in buildings based on a feedback artificial neural network. Energy Build. 37(6), 595–601 (2005)Google Scholar
  7. 7.
    Joy, V.M., Krishnakumar, S.: Optimal design of power sheduling using artificial neural network in an isolated power system. Int. J. Pure Appl. Math. 118(8), 289–294 (2018)Google Scholar
  8. 8.
    Suman, M., Venugopal, M.: ANN-based short term hydrothermal scheduling. RECENT 14(3), 191–195 (2013). (39)Google Scholar
  9. 9.
    Grossi, E., Buscema, M.: Introduction to artificial neural networks. Eur. J. Gastroenterol. Hepatol. 12(19), 1046–1059 (2007)Google Scholar
  10. 10.
    Alsmadi, M., Omar, K., Noah, S.: Back propagation algorithm: the best algorithm among the multi-layer perceptron algorithm. Int. J. Comput. Sci. Netw. Secur. 9(4), 378–383 (2009)Google Scholar
  11. 11.
    Maind, M.S.B., Wankar, M.P.: Research paper on basic of artificial neural network. Int. J. Recent Innov. Trends Comput. Commun. 2, 96–100 (2014)Google Scholar
  12. 12.
    Nawi, N.M., Rehman, M.Z., Aziz, M.A., Herawan, T., Abawajy, J.H.: An accelerated particle swarm optimization based Levenberg Marquardt back propagation algorithm. In: Loo, C.K. et al. (eds.) Neural Information Processing. Lecture Notes in Computer Science, vol. 8835, pp. 245–253. Springer, Cham (2014)Google Scholar
  13. 13.
    Lv, C.: Levenberg–Marquardt backpropagation training of multilayer neural networks for state estimation of a safety-critical cyber-physical system. IEEE Trans. Ind. Inf. 14(8), 3436–3446 (2018)Google Scholar
  14. 14.
    Rao, C.S.: Design of artificial intelligent controller for automatic generation control of two area hydrothermal system. Int. J. Electr. Comput. Eng. 2(2), 183 (2012)Google Scholar
  15. 15.
    Suliman, A., Zhang, Y.: A review on back-propagation neural networks in the application of remote sensing image classification. J. Earth Sci. Eng. 5, 52–65 (2015)Google Scholar
  16. 16.
    Hota, P.K., Chakrabarti, R., Chattopadhyay, P.K.: Short-term hydrothermal scheduling through evolutionary programming technique. Electr. Power Syst. Res. 52(2), 189–196 (1999)Google Scholar
  17. 17.
    Basheer, I.A., Hajmeer, M.: Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods 43(1), 3–31 (2000)Google Scholar
  18. 18.
    Joy, V.M., Krishnakumar, S.: Efficient load scheduling method for power management. Int. J. Sci. Technol. Res. 5(01), 99–101 (2016)Google Scholar
  19. 19.
    Le, K.C., Dinh, B.H., Nguyen, T.: Environmental economic hydrothermal system dispatch by using a novel differential evolution. J. Eng. Technol. Sci. 50(1), 1–20 (2018)Google Scholar
  20. 20.
    Mishra, D.K., Dwivedi, A.K.D., Tripathi, S.P.: Efficient algorithms for load forecasting in electric power system using artificial neural network. Int. J. Latest Res. Sci. Technol. 1(3), 254–258 (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Technology and Applied SciencesM. G. University Research CentreEdappally, KochiIndia

Personalised recommendations