Effectiveness evaluation of discrete macromodelling to forecast power consumption of electric power system component elements

2016;
: pp.45-48
1
Lviv Polytechnic National University
2
Lviv Polytechnic National University
3
Lviv Polytechnic National University

The paper is concerned with a method intended for forecasting electric power consumption using discrete macromodels of daily and annual electric power consumption of defined objects. The method provides the possibility of estimating qualitative characteristics of future electric power consumption based on known prior data. The procedure of develop­ment of a mathematical macromodel for the electric power consumption forecasting by using the evolution algorithms is described; it is based on the discrete autonomous macromodels in the form of state equations using "black box" approach. A discrete auto­nomous macromodel of annual power consumption of a real component element has been developed as a test example for the proposed technique. Effectiveness of the discrete equation apparatus application for power consumption forecasting of electric utilities has been evaluated.

  1. Yu. Kozak, Modification of the Rastrigin’s director cone method, Elektronika i sviaz. Special issue on Problems of Physical and Biomedical Electronics, p. 424, 1997.
  2. E. Salinelli and F. Tomarelli, Discrete Dynamical Models, Basel, Switzerland: Springer International Pub­lishing, 2014.
  3. P. Stakhiv and O. Hoholyuk, Accelerated calcu­lation of transient processes using discrete macromodels of components on example of electric power systems, Tekhnichna elektrodynamika. Special issue on Problems of Modern Electrical Engineering, Kyiv, Part 7, pp. 17–21, 2008. (Ukrainian)
  4. P. Chernenko and O. Martynyuk, Enhancing the Effectiveness of Short-Term Forecasting of Electric Load of United Power System, Tekhnichna elektro­dynamika, no. 1, pp. 63-70, 2012. (Ukrainian)
  5. S. Huang, Short-term load forecasting using thres­hold autoregressive models, IEE Proceedings on Gene­ration, Transmission and Distribution, vol. 144, no. 5, pp. 477-481, 1997.
  6. G. Shumilova, N. Gotman, and T. Startseva, Fore­casting of electric loading using artificial intelligence techniques http://www.energy.komisc.ru/seminar/StShum1.pdf
  7. D. Infield and D. Hill, Optimal smoothing for trend removal in short term electricity demand forecasting, IEEE Trans. on PAS, vol.13. no. 3, pp. 1115 - 1120, 1998.
  8. A. Singh, I. Khatoon, and Md. Muazzam, An Over­view of Electricity Demand Forecasting Techniques, in Proc. National Conf. on Emerging Trends in Electrical, Instrumentation & Communication Engineering, vol. 3, no. 3, pp. 38-45, 2013.