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Short-term load in power system is nonlinear and non-stationary. To cope with the problem that the training error of neural network prediction model is increased while the generalization ability is reduced caused by the large input fluctuation, the rough neurons with upper and lower inputs are introduced into the radial basis function (RBF) neural networks, a power system short-term load forecasting...
Short-term load forecasting is the basis of power system regulation, and it affects many decisions of power system. In order to deal with the challenge of decline in prediction accuracy caused by reduction of cost, and improve the forecasting accuracy and speed, an improved extreme learning machine algorithm, which combines prior knowledge of residential electricity consumption habits is proposed...
Multinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, it is necessary a technique that is precise, trustable and has a short-time processing. This paper proposes two methodologies based on general regression neural networks for short-term multinodal load forecasting. The first...
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