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文件名称:SVM_Short-term-Load-Forecasting2
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- 上传时间：2018-08-02
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文件大小：1.52mb
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本文采用最小二乘支持向量机（LSSVM）模型，根据浙江台州某地区的历史负荷数据和气象数据，分析影响预测的各种因素，总结了负荷变化的规律性，对历史负荷数据中的“异常数据”进行修正，对负荷预测中要考虑的相关因素进行了归一化处理。LSSVM中的两个参数对模型有很大影响，而目前依然是基于经验的办法解决。对此，本文采用粒子群优化算法对模型参数进行寻优，以测试集误差作为判决依据，实现模型参数的优化选择，使得预测精度有所提高。实际算例表明，本文的预测方法收敛性好、有较高的预测精度和较快的训练速度。(this paper adopted particle swarm optimization algorithm to optimized the model parameters, make the test set error as the judgments, realized the optimization of model parameters, maked prediction accuracy improved. Practical examples show that convergence of prediction method was pefect, had a higher prediction accuracy and fast training speed.)
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下载文件列表
SVM_Short-term-Load-Forecasting2/
SVM_Short-term-Load-Forecasting2/AdaptFunc.m
SVM_Short-term-Load-Forecasting2/AdaptFunc1.m
SVM_Short-term-Load-Forecasting2/B2.xls
SVM_Short-term-Load-Forecasting2/B4.xls
SVM_Short-term-Load-Forecasting2/B5.xls
SVM_Short-term-Load-Forecasting2/BaseStepPso.m
SVM_Short-term-Load-Forecasting2/InitSwarm.m
SVM_Short-term-Load-Forecasting2/a23.xls
SVM_Short-term-Load-Forecasting2/a45.xls
SVM_Short-term-Load-Forecasting2/b3.xls
SVM_Short-term-Load-Forecasting2/bdata1.xls
SVM_Short-term-Load-Forecasting2/gaijin.m
SVM_Short-term-Load-Forecasting2/pso.m
SVM_Short-term-Load-Forecasting2/shorttime.m
SVM_Short-term-Load-Forecasting2/~$支持向量机的短期电力负荷预测.doc
SVM_Short-term-Load-Forecasting2/~WRL0001.tmp
SVM_Short-term-Load-Forecasting2/基于PSO优化LSSVM的短期风速预测.pdf
SVM_Short-term-Load-Forecasting2/基于支持向量机的短期电力负荷预测.doc
SVM_Short-term-Load-Forecasting2/数据/
SVM_Short-term-Load-Forecasting2/数据/B2.xls
SVM_Short-term-Load-Forecasting2/数据/B4.xls
SVM_Short-term-Load-Forecasting2/数据/B5.xls
SVM_Short-term-Load-Forecasting2/数据/a23.xls
SVM_Short-term-Load-Forecasting2/数据/a45.xls
SVM_Short-term-Load-Forecasting2/数据/b3.xls
SVM_Short-term-Load-Forecasting2/数据/bdata1.xls
SVM_Short-term-Load-Forecasting2/AdaptFunc.m
SVM_Short-term-Load-Forecasting2/AdaptFunc1.m
SVM_Short-term-Load-Forecasting2/B2.xls
SVM_Short-term-Load-Forecasting2/B4.xls
SVM_Short-term-Load-Forecasting2/B5.xls
SVM_Short-term-Load-Forecasting2/BaseStepPso.m
SVM_Short-term-Load-Forecasting2/InitSwarm.m
SVM_Short-term-Load-Forecasting2/a23.xls
SVM_Short-term-Load-Forecasting2/a45.xls
SVM_Short-term-Load-Forecasting2/b3.xls
SVM_Short-term-Load-Forecasting2/bdata1.xls
SVM_Short-term-Load-Forecasting2/gaijin.m
SVM_Short-term-Load-Forecasting2/pso.m
SVM_Short-term-Load-Forecasting2/shorttime.m
SVM_Short-term-Load-Forecasting2/~$支持向量机的短期电力负荷预测.doc
SVM_Short-term-Load-Forecasting2/~WRL0001.tmp
SVM_Short-term-Load-Forecasting2/基于PSO优化LSSVM的短期风速预测.pdf
SVM_Short-term-Load-Forecasting2/基于支持向量机的短期电力负荷预测.doc
SVM_Short-term-Load-Forecasting2/数据/
SVM_Short-term-Load-Forecasting2/数据/B2.xls
SVM_Short-term-Load-Forecasting2/数据/B4.xls
SVM_Short-term-Load-Forecasting2/数据/B5.xls
SVM_Short-term-Load-Forecasting2/数据/a23.xls
SVM_Short-term-Load-Forecasting2/数据/a45.xls
SVM_Short-term-Load-Forecasting2/数据/b3.xls
SVM_Short-term-Load-Forecasting2/数据/bdata1.xls
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