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基于小波包分析的电力负荷预测算法 被引量:10
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作者 张大海 江世芳 《电力系统及其自动化学报》 CSCD 2004年第2期51-53,84,共4页
提出基于小波包分解和重构的电力负荷预测算法。算法使用具有线性相位的双正交小波对电力负荷数据进行小波包分解和重构 ,然后用神经网络直接对各尺度上的电力负荷分量进行预测 ,最后将各尺度上的预测值相加 ,得到实际负荷预测值。算例... 提出基于小波包分解和重构的电力负荷预测算法。算法使用具有线性相位的双正交小波对电力负荷数据进行小波包分解和重构 ,然后用神经网络直接对各尺度上的电力负荷分量进行预测 ,最后将各尺度上的预测值相加 ,得到实际负荷预测值。算例表明算法具有较高的预测精度 ,优于传统的 BP神经网络 ,有利于分析不同时频区域的电力负荷特性 。 展开更多
关键词 电力负荷预测算法 小波包分析 人工神经网络 小波理论 电力系统
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Optimization of support vector machine power load forecasting model based on data mining and Lyapunov exponents 被引量:7
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作者 牛东晓 王永利 马小勇 《Journal of Central South University》 SCIE EI CAS 2010年第2期406-412,共7页
According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are comput... According to the chaotic and non-linear characters of power load data,the time series matrix is established with the theory of phase-space reconstruction,and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension.Due to different features of the data,data mining algorithm is conducted to classify the data into different groups.Redundant information is eliminated by the advantage of data mining technology,and the historical loads that have highly similar features with the forecasting day are searched by the system.As a result,the training data can be decreased and the computing speed can also be improved when constructing support vector machine(SVM) model.Then,SVM algorithm is used to predict power load with parameters that get in pretreatment.In order to prove the effectiveness of the new model,the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network.It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%,1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension,14-dimension and BP network,respectively.This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting. 展开更多
关键词 power load forecasting support vector machine (SVM) Lyapunov exponent data mining embedding dimension feature classification
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