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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 7910971, 8 pages
Research Article

Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

1College of Automation and Electrical Engineering, Qingdao University, Qingdao 266071, China
2Qingdao Electric Power Company, Qingdao 266002, China

Received 27 March 2016; Revised 18 June 2016; Accepted 11 July 2016

Academic Editor: Antonino Laudani

Copyright © 2016 Zhisheng Zhang and Wenjie Gong. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of -nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.