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The short term load forecasting plays a significant role in the management of power system supply for countries and regions, in particular in cases of insufficient electric energy for increased needs. A back-propagation artificial neural-network (BP-ANN) genetic algorithm (GA) based optimizing technique for improved accuracy of predictions short term loads is proposed. With GA's optimizing and BP-ANN's...
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...
According to the chaotic and non-linear characters of power load data, the model of artificial neural networks ANN based on Lyapunov exponents was established. The time series matrix was established according to the theory of phase-space reconstruction, and then Lyapunov exponents was computed to determine time delay and embedding dimension. Then artificial neural networks algorithm was used to predict...
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