Алгоритмічне забезпечення методу прогнозування обсягів споживання електроенергії з використанням рекурентної нейронної мережі
Journal Title: Математичне моделювання - Year 2017, Vol 1, Issue 1
Abstract
ALGORITHMIC PROVIDING OF THE METHOD FOR FORECASTING OF THE ELECTRICITY CONSUMPTION VOLUME WITH THE USE OF A RECURRENT NEURAL NETWORK Kosukhina O., Tonkonog S.E. Abstract The urgency of the topic of this work lies in the fact that the prediction of electric load is one of the main parameters that determine the mode of power systems operation. The forecasting errors necessarily lead to unreasonable costs in the energy sector. This is due to the fact that the reassessment of future consumption leads to unreasonable over-consumption of all types fuels, and its underestimation to a decrease in the quality of energy supply to consumers. The purpose of this work is to improve the quality of the forecast of hourly electricity consumption by developing algorithmic and software method using recurrent neural network. The following tasks were set in the work: - to analyze the existing methods of time series forecasting; - to construct the algorithm and software of the forecasting method using the recurrent neural network; - apply the developed algorithmic and software for forecasting of hourly electricity consumption. As a result of the work, the algorithmic and software of the method of forecasting of time series using the recurrent neural network was developed. The developed software was used to forecast hourly electricity consumption. It is proved that the prediction error is valid and does not exceed the error of studying. References [1] Tzafestas S. Computational intelligence techniques for short-term electric load forecasting / S. Tzafestas, E. Tzafestas // Journal of Intelligent and Robotic Systems. – 2001. – 31. – P. 7–68. [2] Mandic D.P. Recurrent Neural Networks for Prediction/ D.P. Mandic, J.A. Chambers. – Chichester: John Wiley&Sons, 2001. – 285 p. [3] Williams R.J. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks / R.J. Williams,D. Zipser // Neural Computation. – 1989. – 1. – P. 270–280. [4] Elman J.L. Finding structure in time / J.L. Elman //Cognitive Science. – 1990. – 14. – P. 179–211. [5] Jordan M. Constrained supervised learning /M. Jordan // Journal of Mathematical Psychology. – 1992. – 36. – P. 396–452. [6] Vikko N., Lautala, P. Short-term electric power production scheduling using simulated annealing algorithm: Proc. of the IASTED Inter. Sym / N. Vikko, P. Lautala // ACTA Press, Anaheim, CA, USA, 1990. [7] Chumachenko Е. I. Algorithm for solving the problem of forecasting / Е. I. Chumachenko, V. S. Horbatuk // Artificial Intelligence. – 2012. – № 2. – p.p. 24–30 (in Russian).
Authors and Affiliations
О. С. Косухіна, С. Є. Тонконог
Keywords
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