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SVM_Short-term-Load-Forecasting

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  • Update : 2012-11-26
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first expounds the recent application research of load forecasting, summarized the characteristics of load forecasting and influencing factors, summed up common methods of short-term load forecasting, and analyzed the advantages and disadvantages of each method then introduced statistical learning theory and the principle of SVM as the basis of support vector machine (SVM ) theory, SVM regression model is derived this paper adopted least squares support vector machine (LSSVM) model, according to the historical load data and meteorological data of a certain area of Zhejiang Taizhou, Analysised the various factors affecting the forecast, summed up the regularity of load change , amended "outliers" in the historical load data,the load forecasting factors to be considered were normalized. The two parameters of LSSVM have a significant impact on the model, but it is still soluted based on the experience currently. So, this paper adopted particle swarm optimization algorithm to optimized
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数据\a23.xls
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AdaptFunc.m
AdaptFunc1.m
BaseStepPso.m
gaijin.m
InitSwarm.m
pso.m
shorttime.m
基于支持向量机的短期电力负荷预测.doc
数据
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