软计算方法和数据挖掘理论在电力系统负荷预测中的应用
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摘要
数据挖掘技术能够从大量数据中提取人们感兴趣的潜在知识和信息，软计算是一种创建计算智能系统的有效方法。本文将两者相结合，完成负荷预测过程中的两个主要任务：电力负荷坏数据处理和多因素负荷预测建模。计算过程完全基于数据自动进行，提高了模型的智能化和科学性。
本文概述了数据挖掘技术的有关内容，着重阐述其中两种重要的思想：分类方法与聚类分析，并详述它们的具体实现方法。本文关于负荷预测问题的研究始终贯穿了这两种思想。
神经网络和模糊系统是软计算的重要基础，它们是设计智能系统的精髓。本文详细讨论了BP网、Kohonen网两种神经网络和TSK型模糊推理系统的原理、结构和算法等基本问题，其中前两者用于对负荷坏数据的处理，后者用在多因素负荷预测建模。
负荷坏数据辨识是由负荷曲线抗差聚类和坏数据曲线模式分类两个顺序的过程组成的；本文通过对Kohonen网的抗差聚类和BP网模式分类的效果分析，设计由这两种网络组合而成的神经网络模型，较好地完成了坏数据辨识的任务。
本文以模糊推理系统为基础，构建自适应神经—模糊系统建立预测模型。建模过程中解决了两个主要问题：模糊建模中的结构辨识问题和ANFIS系统参数辨识的收敛性问题。本文采用决策树分类方法完成结构辨识的任务，初步找出负荷变化的模式，有效减少了系统需要优化调整的参数数量；采用拟牛顿优化方法，较好地解决了大规模参数优化问题。
模糊建模的难点是结构辨识中输入变量的选择和输入空间的划分。本文采用CART算法来解决结构辨识问题，它能够剔除无关变量并将输入空间划分成树状结构；在此基础上设计适当的隶属度函数，提高了参数辨识过程的精度和速度。
Data mining distills connotative knowledge and information from abundant data, while soft computing is an effective mothed to establish intelligent computing systems. This dissertation combines the two motheds to accomplish two main tasks: outliers processing in load data and multi-factor STLF (short term load forecasting) system modeling. The algorithms operate based on data completely, and are more intelligent and scientific.
The dissertation layes stress on two main thoughts of data mining: classification and cluster analysis, some methods of which are discussed.
Neural networks and fuzzy systems lay the two main foundations of soft computing. The principles, constructures and algorithms of BP and Kohonen network and the TSK fuzzy model are discussed. The two neural networks are combined to processing the outliers in load data, while the fuzzy model is the kernel of a multi-factor load forecasting system.
The outlier identification is divided into two sequential parts: the robust day-load-curves cluster and the bad curve pattern classifcation. By analysising the effects of Kohonen network clustering and BP network classifcation, the dissertation designs an outlier identification model comprising these two kinds of neural network and implements the tasks of bad data identifications and adjustments.
A STLF system is modeled based on ANFIS (Adaptive Neural-Fuzzy Inference System). Two main problems are sovled: fuzzy system structure identifications and ANFIS parameters identifications. A decision tree classifier is used to deal with the first task, which can find the load pattern preliminarily, and reduces the number of parameters to be adjusted. The Quasi-Newton method is applied to solving the second problem, which is suitable to optimizing large number of parameters.
Difficulties of fuzzy modeling are the variables selection and the input space division in the fuzzy structure identification. CART can eliminate the unnecessary variables, and then divides the input space as a tree-shape. Appropriate membership functions improve the convergency of the parameters identification.
本文概述了数据挖掘技术的有关内容，着重阐述其中两种重要的思想：分类方法与聚类分析，并详述它们的具体实现方法。本文关于负荷预测问题的研究始终贯穿了这两种思想。
神经网络和模糊系统是软计算的重要基础，它们是设计智能系统的精髓。本文详细讨论了BP网、Kohonen网两种神经网络和TSK型模糊推理系统的原理、结构和算法等基本问题，其中前两者用于对负荷坏数据的处理，后者用在多因素负荷预测建模。
负荷坏数据辨识是由负荷曲线抗差聚类和坏数据曲线模式分类两个顺序的过程组成的；本文通过对Kohonen网的抗差聚类和BP网模式分类的效果分析，设计由这两种网络组合而成的神经网络模型，较好地完成了坏数据辨识的任务。
本文以模糊推理系统为基础，构建自适应神经—模糊系统建立预测模型。建模过程中解决了两个主要问题：模糊建模中的结构辨识问题和ANFIS系统参数辨识的收敛性问题。本文采用决策树分类方法完成结构辨识的任务，初步找出负荷变化的模式，有效减少了系统需要优化调整的参数数量；采用拟牛顿优化方法，较好地解决了大规模参数优化问题。
模糊建模的难点是结构辨识中输入变量的选择和输入空间的划分。本文采用CART算法来解决结构辨识问题，它能够剔除无关变量并将输入空间划分成树状结构；在此基础上设计适当的隶属度函数，提高了参数辨识过程的精度和速度。
Data mining distills connotative knowledge and information from abundant data, while soft computing is an effective mothed to establish intelligent computing systems. This dissertation combines the two motheds to accomplish two main tasks: outliers processing in load data and multi-factor STLF (short term load forecasting) system modeling. The algorithms operate based on data completely, and are more intelligent and scientific.
The dissertation layes stress on two main thoughts of data mining: classification and cluster analysis, some methods of which are discussed.
Neural networks and fuzzy systems lay the two main foundations of soft computing. The principles, constructures and algorithms of BP and Kohonen network and the TSK fuzzy model are discussed. The two neural networks are combined to processing the outliers in load data, while the fuzzy model is the kernel of a multi-factor load forecasting system.
The outlier identification is divided into two sequential parts: the robust day-load-curves cluster and the bad curve pattern classifcation. By analysising the effects of Kohonen network clustering and BP network classifcation, the dissertation designs an outlier identification model comprising these two kinds of neural network and implements the tasks of bad data identifications and adjustments.
A STLF system is modeled based on ANFIS (Adaptive Neural-Fuzzy Inference System). Two main problems are sovled: fuzzy system structure identifications and ANFIS parameters identifications. A decision tree classifier is used to deal with the first task, which can find the load pattern preliminarily, and reduces the number of parameters to be adjusted. The Quasi-Newton method is applied to solving the second problem, which is suitable to optimizing large number of parameters.
Difficulties of fuzzy modeling are the variables selection and the input space division in the fuzzy structure identification. CART can eliminate the unnecessary variables, and then divides the input space as a tree-shape. Appropriate membership functions improve the convergency of the parameters identification.
引文
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