【作者】 耿艳；

【导师】 韩学山；

【作者基本信息】 山东大学 ， 电力系统及其自动化， 2008， 硕士

【摘要】 短期负荷预测是电力系统运行调度决策的前提,准确地进行这一预测会使电力系统的控制有的放矢,因此其研究是有价值的。论文总结了短期负荷预测的特点,归纳了其常用输入量的选取方法。并基于统计学方法,对历史负荷数据进行了有效处理,如异常数据的修正、规格化等。同时针对最小二乘支持向量机预测的输入变量通常由经验选择所造成预测模型适应性不好的问题,采用粗糙集理论进行了预处理,对各条件属性进行约简分析,其属性约简采用二进制编码的遗传算法进行寻优,可以自动地从含有不相关量和冗余量的待选输入变量中选择出与负荷密切相关的因素,作为最小二乘支持向量机的有效输入变量。从而实现了输入变量的优化选择,减少了预测模型建立过程中对经验的依赖,提高了模型的适应性。在此基础上,由最小二乘支持向量机模型中的两个参数,分析出该参数选择对模型有很大影响,而目前依然是基于经验的办法解决。对此,提出采用遗传算法对最小二乘支持向量机的模型参数进行寻优,实现了模型参数的优化选择,并建立了相应的预测模型,使其有所改进。综合上述研究,建立了结合粗糙集理论和遗传算法的最小二乘支持向量机短期负荷预测模型和算法,并编制了程序。在该模型和算法中,粗糙集用于历史数据的预处理,并就各条件属性进行约简分析,以确定与负荷密切相关的因素,作为最小二乘支持向量机的输入变量;在预测过程中,遗传算法用于对模型参数进行自适应寻优,以尽可能提高负荷预测精度。山东电网的实际预测分析表明其有效性。更多还原

【Abstract】 Short-term load forecasting is the precondition of operation, dispatch and decision-making of power system. Accurate short term load forecasting has a significant impact on control of power system, so this research is valuable.This dissertation sums up the characteristics of short-term load forecasting, and concludes its common selection of input variables. Based on statistics, the historical data are preprocessed such as disorder data are removed and data are normalized. The input vector is usually selected with human experience in least squares support vector machine(LS-SVM) forecasting model. This makes the adaptability of the model not good. In this dissertation, rough sets are used to analyze the condition attributes, and the attributes closed to load can be selected from the candidates set which contains irrelevant and redundant variables automatically, which are then applied to the LS-SVM as the effective input vector to forecast load. Meanwhile binary genetic algorithm is used to reduce the attributes. So this method can realize the selection of input variables optimization, reduce the dependence on experience in the course of prediction model established and enhance the adaptability of the model.Based on this, two important parameters of LS-SVM model are analyzed inducing that model parameters influence the performance of LS-SVM evidently. But at present parameters are generally determined by experience or crossing test. So this dissertation proposes to use floating genetic algorithm for adaptively optimizing the parameters of LS-SVM, and establish the forecasting model.Integrating the above research, for short-term load forecasting problem, an effective model and algorithm of LS-SVM combining rough sets and genetic algorithm are proposed and programmed. In this model and algorithm, the historical data are preprocessed by rough sets to analyze the condition attributes and obtain the factors closely related with load, which are then applied to the LS-SVM as the input vector to forecast load. During the model training process, it also uses floating genetic algorithm for adaptively optimizing the parameters of LS-SVM to improve the load forecasting accuracy. Shandong power grid is analyzed to exhibit the effectiveness of the proposed approach.更多还原