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Electricity demand forecasting is a nonlinear and complex problem. It consists of three levels, including long-term forecast for new power plant planning, medium-term forecast for maintenance scheduling and inventory of fuel, and short-term forecast for daily operations. There are many statistical forecasting techniques applied to short term load forecasting, such as Stochastic Time series, Regression...
In short term load forecasting using Neural Networks, when the training data is contaminated with high noise, this noise is mapped into network's weights, and causes increasing of forecasting error. This forecasting error make us apply some methods to increase accuracy in neural net. In this paper, load forecasting of such power systems is done based on employing two methods: Pruning algorithm and...
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