【作者】 沈海澜；

【导师】 蒋外文； 王加阳；

【作者基本信息】 中南大学 ， 计算机应用技术， 2003， 硕士

【摘要】 本文在概要介绍电力负荷预测研究现状之后，首先对电力负荷预测系统架构模式展开讨论，并在其基础上重点展开了基于数据挖掘技术的电力负荷预测研究。主要工作由两大部分组成： 第一部分包括第二章，主要展开了电力负荷预测系统架构模式的研究。我们通过对常见的电力负荷预测系统架构模式进行分析，从智能决策支持的角度，提出了一个新型通用的电力负荷预测系统架构模式——基于数据挖掘技术的电力负荷预测系统架构模式。 第二部分包括第三章至第五章，重点是在第一部分所建立的通用框架的基础上，从数据挖掘的角度展开电力负荷预测研究。 第三章，我们从负荷预测中的知识支持需求出发，重点针对电力负荷预测建模关键属性选择问题展开讨论，提出了基于信息熵的负荷预测最佳属性集发现方法。 第四章，我们对神经网络应用于电力负荷预测的优势及目前存在的不足展开讨论，提出了基于模糊遗传神经网络的电力负荷预测方法。 第五章，我们针对基于规则推理的专家系统负荷预测方法存在的瓶颈问题展开讨论，基于关联规则挖掘技术实现知识获取，提出了基于模糊关联规则挖掘的电力负荷预测方法。 论文最后在第六章对全文所开展的研究工作进行总结，并指明了未来的研究方向。更多还原

【Abstract】 After briefly describing the current research situation of electric power load forecasting, the dissertation firstly discusses the architecture model of electric power load forecasting system. And then, the dissolution focuses its emphasis on the research of electric power load forecasting based on data mining technology. Main work of this research consists of two parts:Part I , including chapter 2, studies the architecture model of electric power load forecasting system. By analyzing the current architecture model, we put forward on anew and universal architecture model-an architecture model of electric powerload forecasting system based on data mining technology.Part II is composed of chapter 3 to chapter 6. We mainly discuss electric power load forecasting based on data mining technology.Firstly, in chapter 3, we mainly discuss the question of the selection of key attributes in load forecasting model-building. We put forward on a method of mining best attribute set using information entropy.Secondly, in chapter 4, by the analyzing the advantages and the key points of using artificial neural network model for electric power load forecasting, we put forward on a method of electric power load forecasting based on fuzzy genetic neural network.Thirdly, in chapter 5, to solve the main problem of electric power load forecasting based on expert system, a method of electric power load forecasting based on fuzzy association rules mining is put forward on.In chapter 6, research results of this dissertation are summarized, and some directions for further research are also provided.更多还原