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中文摘要：
根据力量负担数据的混乱、非线性的字符的意见，时间系列矩阵与阶段空间重建的理论被建立，然后有混乱时间系列的 Lyapunov 代表被计算决定时间延期和嵌入的尺寸。由于数据的不同特征，数据采矿算法被进行分类数据进不同的组。冗余的信息被数据采矿技术，和有预报的天的高度类似的特征被系统寻找了的历史的负担的优点消除。作为结果，训练数据能被减少，当构造支持时，计算速度能也被改进向量机器(SVM ) 模型。然后， SVM 算法被用来与在预告的处理得到的参数预言力量负担。以便证明新模型，的有效性有数据采矿 SVM 算法的计算与单个 SVM 和背繁殖网络的相比。新 DSVM 算法有效地分别地为 11 尺寸， 14 尺寸和 BP 网络的二种随机的尺寸与 SVM 相比在 0.75% ， 1.10% 和 1.73% 改进预报精确性，这能被看见。这显示在短期的力量的 DSVM 获得完成式改进效果装载预报。
英文摘要：
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.
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同项目期刊论文
Multi-objective coordination optimal model for new power intelligence center based on hybrid algorit
Research on the wavelet neural networks based on data mining and its application in daily load forec
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