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To settle the problem which the precision and generalization performance of forecast model is affected easily by input variable, the method which reconstructs the original input space of back-propagation neural network by principal component analysis that can eliminate the relevance of value is researched. The method can not only reduce duplicated information but also extract the leading factors....
Load forecasting model which synthetically considers every kind of impact factor is created in this paper. The input load data and temperature are normalized, and weather condition variable is quantitatively transacted. The applications of the BP (Back-Propagation Network) neural network algorithm and the neural network toolbox in MATLAB 7.0 software achieve load forecasting. The experimental result...
The weather factors with on-site EMC measurement may differ a lot from the standard calibration condition, so it is necessary to analyze the impact of weather factors, such as temperature, humidity and atmosphere pressure, on the measurement accuracy of the antennas. Based on experimental study, digital signal processing, improved vector fitting methods and artificial neural networks (ANN), a comprehensive...
This paper optimizes the wavelet neural networks with genetic algorithms which has the optimization of the overall search capabilities, and establishes the model of wavelet neural networks based on genetic algorithms. It overcomes the shortcomings of BP neural network for their own, and it can get higher accuracy and faster convergence. The examples also show that the model can improve forecast accuracy...
An improved BP Neural Network with additional momentum and adaptive learning is proposed in the paper to predict the growth rate of electricity consumption in China. Matlab7 is used as modeling tool to design the model. Current year GDP growth, electric power consumption growth and growth rate of secondary industry are taken as input variables while next year electric power consumption growth is predicted...
Because power loads are influenced by various factors, and the changes of power load presents are complicate, the traditional forecasting methods are always not satisfied. According to the random-increase and non-linearity fluctuation of residual series, gray neural network forecasting can reflect the increase character and non-linearity relationship. This paper using the improved ACO method as the...
In order to establish a high accuracy forecasting model for short-term electric power load, this paper made a change to grey differential equation utilizing the fundamental theorem of discrete time function. Through mapping the parameters of the equation into the BP neural network, giving the corresponding parameters when the sequence sample of load was converged in the network. In this case, optimizing...
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