Abstract
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.
Highlights
Short-term load forecasting (STLF) is a key basic research project
The STLF model based on the fusion of Phase Space Reconstruction Theory (PSRT) and Quantum Chaotic Neural Networks (QCNN) is constructed in this paper
QCNN is composed of quantum neurons and chaotic neurons, and quantum computation and chaotic mechanism are integrated into QCNN
Summary
Short-term load forecasting (STLF) is a key basic research project It has an important role in the economy, reliability, and operation management of power system. In the early stage of STLF, many forecasting methods based on mathematical statistics theory are put forward [4, 5] These methods are not suitable for the prediction of dynamic load time series. The fusion of quantum theory and artificial neural network has been applied in many fields [9,10,11]. The STLF model based on the fusion of Phase Space Reconstruction Theory (PSRT) and Quantum Chaotic Neural Networks (QCNN) is constructed in this paper. The simulation results show that the STLF model based on the fusion of PSRT and QCNN has good forecasting performance
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