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Short term load forecasting: A dynamic neural network based genetic algorithm optimization

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5 Author(s)
Yan Wang ; Coll. of I. S. & E, Northeastern Univ. & Inst. of Eng, Shenyang, China ; Ojleska, V. ; Yuanwei Jing ; Gugulovska, T.K.
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The short term load forecasting plays a significant role in the management of power system supply for countries and regions, in particular in cases of insufficient electric energy for increased needs. A back-propagation artificial neural-network (BP-ANN) genetic algorithm (GA) based optimizing technique for improved accuracy of predictions short term loads is proposed. With GA's optimizing and BP-ANN's dynamic capacity, the weighted GA optimization is realized by selection, crossing and mutation operations. The performance of the proposed technique has been tested using load time-series from a real-world electrical power system. Its prediction has been compared to those of obtained by only back-propagation based neural-network techniques. Simulation results demonstrated that the here proposed technique possesses superior performance thus guarantees better forecasting.

Published in:

Power Electronics and Motion Control Conference (EPE/PEMC), 2010 14th International

Date of Conference:

6-8 Sept. 2010