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
In order to improve the accuracy of the multiple load forecasting of a regional integrated energy sy...
Short-term load forecasting (STLF) plays a very important role in improving the economy and stabilit...
A unique technique based on chaos theory and artificial neural networks (ANN) is developed to analys...
Abstract:- The nonlinear theories of load forecasting, such as the applications of neural network an...
AbstractTo tackle with the premature matter of particles when seeking optimization in local small sp...
In order to realize the predicting and positioning of short-term load inflection point, this paper m...
Hybridizing evolutionary algorithms with a support vector regression (SVR) model to conduct the elec...
Accurate short-term energy load forecasting has a considerable influence on the economic scheduling ...
In last few decades, short-term load forecasting (STLF) has been one of the most important research ...
Recently, a large number of distributed photovoltaic (PV) power generations have been connected to t...
Accurate short-term load forecasting can ensure the safe and stable operation of power grids, but th...
Accurate electricity forecasting is still the critical issue in many energy management fields. The a...
In existing forecasting research papers support vector regression with chaotic mapping function and ...
In this paper, we focus on the accuracy improvement of short-term load forecasting, which is useful ...
Compared with a large power grid, a microgrid electric load (MEL) has the characteristics of strong ...
In order to improve the accuracy of the multiple load forecasting of a regional integrated energy sy...
Short-term load forecasting (STLF) plays a very important role in improving the economy and stabilit...
A unique technique based on chaos theory and artificial neural networks (ANN) is developed to analys...
Abstract:- The nonlinear theories of load forecasting, such as the applications of neural network an...
AbstractTo tackle with the premature matter of particles when seeking optimization in local small sp...
In order to realize the predicting and positioning of short-term load inflection point, this paper m...
Hybridizing evolutionary algorithms with a support vector regression (SVR) model to conduct the elec...
Accurate short-term energy load forecasting has a considerable influence on the economic scheduling ...
In last few decades, short-term load forecasting (STLF) has been one of the most important research ...
Recently, a large number of distributed photovoltaic (PV) power generations have been connected to t...
Accurate short-term load forecasting can ensure the safe and stable operation of power grids, but th...
Accurate electricity forecasting is still the critical issue in many energy management fields. The a...
In existing forecasting research papers support vector regression with chaotic mapping function and ...
In this paper, we focus on the accuracy improvement of short-term load forecasting, which is useful ...
Compared with a large power grid, a microgrid electric load (MEL) has the characteristics of strong ...
In order to improve the accuracy of the multiple load forecasting of a regional integrated energy sy...
Short-term load forecasting (STLF) plays a very important role in improving the economy and stabilit...
A unique technique based on chaos theory and artificial neural networks (ANN) is developed to analys...