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

2017 ◽  
Vol 2017 ◽  
pp. 1-7
Author(s):  
Zhisheng Zhang
Keyword(s):  
Phase Space ◽  
Load Forecasting ◽  
Phase Space Reconstruction ◽  
Short Term ◽  
Chaotic Neural Networks ◽  
Model Based ◽  
Proposed Model ◽  
Short Term Load Forecasting ◽  
Hidden Layer ◽  
Hidden Nodes

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.

scholarly journals Nearest Neighbors Time Series Forecaster Based on Phase Space Reconstruction for Short-Term Load Forecasting

Energies ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 5309
Author(s):  
Jose R. Cedeño González ◽  
Juan J. Flores ◽  
Claudio R. Fuerte-Esquivel ◽  
Boris A. Moreno-Alcaide
Keyword(s):  
Phase Space ◽  
National Level ◽  
Load Forecasting ◽  
Nearest Neighbors ◽  
Electric Utility ◽  
Phase Space Reconstruction ◽  
Support Vector ◽  
Short Term ◽  
Essential Information ◽  
Short Term Load Forecasting

Load forecasting provides essential information for engineers and operators of an electric system. Using the forecast information, an electric utility company’s engineers make informed decisions in critical scenarios. The deregulation of energy industries makes load forecasting even more critical. In this article, the work we present, called Nearest Neighbors Load Forecasting (NNLF), was applied to very short-term load forecasting of electricity consumption at the national level in Mexico. The Energy Control National Center (CENACE—Spanish acronym) manages the National Interconnected System, working in a Real-Time Market system. The forecasting methodology we propose provides the information needed to solve the problem known as Economic Dispatch with Security Constraints for Multiple Intervals (MISCED). NNLF produces forecasts with a 15-min horizon to support decisions in the following four electric dispatch intervals. The hyperparameters used by Nearest Neighbors are tuned using Differential Evolution (DE), and the forecaster model inputs are determined using phase-space reconstruction. The developed models also use exogenous variables; we append a timestamp to each input (i.e., delay vector). The article presents a comparison between NNLF and other Machine Learning techniques: Artificial Neural Networks and Support Vector Regressors. NNLF outperformed those other techniques and the forecasting system they currently use.

scholarly journals An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network

2019 ◽  
Vol 9 (7) ◽  
pp. 1487 ◽  
Author(s):  
Fei Mei ◽  
Qingliang Wu ◽  
Tian Shi ◽  
Jixiang Lu ◽  
Yi Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Load Forecasting ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Forecasting Model ◽  
Model Based ◽  
Net Load

Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term forecasting model based on phase space reconstruction and deep neural network (DNN) is proposed, which can be divided into two steps. First, the phase space reconstruction of the net load time series data is performed using the C-C method. Second, the reconstructed data is fitted by the DNN to obtain the predicted value of the net load. The performance of this model is verified using real data. The accuracy is high in forecasting the net load under high PV penetration rate and different weather conditions.

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