本文選題：數(shù)據(jù)挖掘 + 需求響應(yīng)�。� 參考：《華北電力大學(北京)》2017年碩士論文

【摘要】：隨著用電信息采集系統(tǒng)的飛速發(fā)展,用電數(shù)據(jù)也日益增多,將數(shù)據(jù)挖掘技術(shù)應(yīng)用于用電數(shù)據(jù)分析可以有效獲取用戶用電的相關(guān)規(guī)律與模式信息,支持用電服務(wù)個性化、差異化的需求,為用電智能化奠定基礎(chǔ)。而如何有效地進行用電信息的數(shù)據(jù)挖掘以及使用數(shù)據(jù)挖掘所得信息支撐智能電網(wǎng)以及智能用電的建設(shè)成為迫切需要解決的問題。本文針對以上問題,從用戶用電數(shù)據(jù)中隱藏著用戶的用電行為習慣著手,使用聚類分析方法對這些用電數(shù)據(jù)進行挖掘并識別用戶用電行為模式,并以此支撐用戶智能用電需求響應(yīng)策略。首先,本論文針對數(shù)據(jù)挖掘過程中的特征提取步驟進行優(yōu)化,設(shè)計了一種基于信息熵與特征相關(guān)系數(shù)的用電特征優(yōu)選策略,通過選出與用戶用電模式關(guān)系緊密且獨立性高的特征集來降低聚類計算過程的復(fù)雜性并改善聚類分析的效果。然后在特征優(yōu)選的基礎(chǔ)上使用一種初始聚類中心改善的k-mean聚類方法完成用電數(shù)據(jù)的聚類分析,并完成用戶用電行為聚類分析的仿真實驗,實驗證明本文所提方法和策略能有效識別不同用電模式的用戶。最后,本文使用聚類分析所得結(jié)果去實現(xiàn)智能用電需求響應(yīng)策略的優(yōu)化。通過針對不用用電模式的用戶的分類調(diào)度,實現(xiàn)用戶智能用電需求響應(yīng)效果的優(yōu)化,以及降低其計算的復(fù)雜度。同時方法基于分布式計算的思想,將優(yōu)化過程拆解至各類用戶,以充分利用用戶資源實現(xiàn)互動調(diào)度過程,從而有效地提高計算效率并保護用戶信息的安全性。
[Abstract]:With the rapid development of power information acquisition system and the increasing number of power data, the application of data mining technology in power data analysis can effectively obtain the relevant laws and mode information of users' electricity consumption, and support the individualization of electricity service. The demand of differentiation lays the foundation for the intelligent use of electricity. How to effectively mine the power information and how to use the data mining information to support the smart grid and the construction of smart electricity has become an urgent problem to be solved. In view of the above problems, this paper starts with the user's electric behavior habits hidden in the user's power consumption data, and uses the clustering analysis method to mine these data and to identify the user's electric behavior pattern. And to support the user intelligent demand response strategy. Firstly, this paper optimizes the feature extraction process in the process of data mining, and designs a strategy based on information entropy and feature correlation coefficient. In order to reduce the complexity of the clustering calculation process and improve the effect of clustering analysis, the feature sets which are closely related to the user power consumption mode and which are highly independent are selected to reduce the complexity of the clustering calculation process. Then, based on the feature selection, a k-mean clustering method with improved initial clustering center is used to complete the clustering analysis of power consumption data, and the simulation experiment of the user's electricity behavior clustering analysis is completed. Experimental results show that the proposed method and strategy can effectively identify users with different power consumption modes. Finally, the results of clustering analysis are used to optimize the intelligent demand response strategy. According to the classified scheduling of users without electricity consumption mode, the user intelligent demand response effect is optimized and the computational complexity is reduced. At the same time, based on the idea of distributed computing, the optimization process is broken down to all kinds of users, in order to make full use of user resources to realize interactive scheduling process, so as to improve the efficiency of computing and protect the security of user information effectively.
【學位授予單位】：華北電力大學(北京)
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.13;TM73

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本文編號：1831571