Short-Term Load Forecasting Model Based on the Fusion of PSRT and QCNN

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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Bibliographic Details
Main Author: Zhisheng Zhang
Format: Article
Language:English
Published: Hindawi Limited 2017-01-01
Series:Mathematical Problems in Engineering
Online Access:http://dx.doi.org/10.1155/2017/3485182
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spelling doaj-b15ebd6119cb4f8f9e158d8bd3c716712020-11-24T23:02:00ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472017-01-01201710.1155/2017/34851823485182Short-Term Load Forecasting Model Based on the Fusion of PSRT and QCNNZhisheng Zhang0School of Automation and Electrical Engineering, Qingdao University, Qingdao 266071, ChinaShort-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.http://dx.doi.org/10.1155/2017/3485182
collection DOAJ
language English
format Article
sources DOAJ
author Zhisheng Zhang
spellingShingle Zhisheng Zhang
Short-Term Load Forecasting Model Based on the Fusion of PSRT and QCNN
Mathematical Problems in Engineering
author_facet Zhisheng Zhang
author_sort Zhisheng Zhang
title Short-Term Load Forecasting Model Based on the Fusion of PSRT and QCNN
title_short Short-Term Load Forecasting Model Based on the Fusion of PSRT and QCNN
title_full Short-Term Load Forecasting Model Based on the Fusion of PSRT and QCNN
title_fullStr Short-Term Load Forecasting Model Based on the Fusion of PSRT and QCNN
title_full_unstemmed Short-Term Load Forecasting Model Based on the Fusion of PSRT and QCNN
title_sort short-term load forecasting model based on the fusion of psrt and qcnn
publisher Hindawi Limited
series Mathematical Problems in Engineering
issn 1024-123X
1563-5147
publishDate 2017-01-01
description 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.
url http://dx.doi.org/10.1155/2017/3485182
work_keys_str_mv AT zhishengzhang shorttermloadforecastingmodelbasedonthefusionofpsrtandqcnn
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