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When neural networks are used to forecast short-term power load, it can learn the experience by training and generate mapping rules, but these rules are not directly understood in the network. By using the method of integrating neural networks and fuzzy logic, neural networks only settle historical load information. Moreover, fuzzy logic considers the factors which have great effect to load varying,...
A short-term load forecasting model is adopted with a combined method. The model not only summarizes virtues and defects of neural networks and fuzzy system, but also considers that power system load has characteristics of basic load heft and variability load heft. It uses learned capability of neural networks to complete forecasting work of basic heft for power load. Other effect factors that cause...
It is very important to select input variables when the support vector machine forecasting model is proposed. The input variables selection for short-term load forecasting is relevant to the performance of support vector machine forecasting. By using the correlation coefficient idea on input variables selection for support vector machine short-term load forecasting, a systemic and operable method...
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