Short-Term Load Forecasting Model Based on the Fusion of PSRT and QCNN
Short-term load forecasting (STLF) model based on the fusion of Phase Space Reconstruction Theory (PSRT) and Quantum Chaotic Neural Networks (QCNN) was proposed. The quantum computation and chaotic mechanism were integrated into QCNN, which was composed of quantum neurons and chaotic neurons. QCNN h...
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| Format: | Article |
| Language: | English |
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Hindawi Limited
2017-01-01
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| Series: | Mathematical Problems in Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/3485182 |
| Summary: | Short-term load forecasting (STLF) model based on the fusion of Phase Space Reconstruction Theory (PSRT) and Quantum Chaotic Neural Networks (QCNN) was proposed. The quantum computation and chaotic mechanism were integrated into QCNN, which was composed of quantum neurons and chaotic neurons. QCNN has four layers, and they are the input layer, the first hidden layer of quantum hidden nodes, the second hidden layer of chaotic hidden nodes, and the output layer. The theoretical basis of constructing QCNN is Phase Space Reconstruction Theory (PSRT). Through the actual example simulation, the simulation results show that proposed model has good forecasting precision and stability. |
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| ISSN: | 1024-123X 1563-5147 |