本文選題：智能電網　切入點：大數(shù)據(jù)　出處：《華北電力大學》2017年碩士論文　論文類型：學位論文

【摘要】：隨著電網規(guī)模的不斷擴大,加之智能終端設備的安裝覆蓋率逐年提高,使得電網的業(yè)務數(shù)據(jù)正以爆炸性的趨勢增長,如何利用數(shù)據(jù)挖掘技術,在海量的電網數(shù)據(jù)中挖掘出有價值的信息,成為當前電力系統(tǒng)分析中的一個挑戰(zhàn)性問題。另一方面,隨著市場經濟的發(fā)展,電網公司正在逐漸轉化為經營型企業(yè),如何對用電用戶的行為進行分析和預測,并為其提供個性化的電力服務,成為電網公司亟待解決的問題。因此,對電力大數(shù)據(jù)進行分析和研究,將有利于提高電網公司的經營管理水平和建立堅強的智能電網。本文研究了國外大數(shù)據(jù)的發(fā)展概況以及我國大數(shù)據(jù)的發(fā)展現(xiàn)狀和行業(yè)動態(tài),重點研究了當今電力大數(shù)據(jù)現(xiàn)狀以及天津市電力公司對業(yè)務場景的需求分析,提出了基于電力大數(shù)據(jù)的用戶行為分析及可視化展示平臺,該平臺實現(xiàn)了用電客戶分類和用戶用電量預測功能,并對分析結果進行可視化展示。本文首先通過分析目標用電客戶的用電行為、用電習慣和用電規(guī)律,將數(shù)據(jù)挖掘與用電客戶分類相結合,完成了電力客戶分類整體模型的設計和指標體系的構建。將聚類算法與Hadoop分布式處理框架相結合,給出了基于Hadoop的并行化K-means算法,利用電網公司營銷側數(shù)據(jù),通過分析電力客戶的用電量情況、信用情況和價值創(chuàng)造等,實現(xiàn)了用電客戶分類的功能。用戶的用電量行為在具有自身規(guī)律的同時,也受著外界因素的影響。將關聯(lián)規(guī)則挖掘算法與Hadoop分布式處理框架相結合,給出了基于Hadoop的并行化Apriori算法,結合會對用戶用電量行為帶來影響的經濟、氣溫等影響因素,通過分析相關因素對用戶用電量的影響和關聯(lián)性,實現(xiàn)了用戶用電量預測功能。最后通過ExtJS技術,將挖掘出的用戶行為進行可視化展示,輔助電網公司提高自身的業(yè)務水平和經營管理水平,使電網公司能夠更有針對性的開展市場營銷業(yè)務并提高其客戶服務能力。
[Abstract]:With the continuous expansion of power grid scale and the increasing installation coverage rate of smart terminal equipment, the service data of power grid is increasing explosively. How to use data mining technology, Mining valuable information from massive power grid data has become a challenging problem in power system analysis. On the other hand, with the development of market economy, power grid companies are gradually transforming into operating enterprises. How to analyze and predict the behavior of power users and provide them with personalized power services has become an urgent problem for power grid companies. It will help to improve the management level of power grid companies and establish a strong smart grid. This paper studies the general situation of big data's development abroad, the current situation of the development and the industry trends of our country big data. This paper mainly studies the current situation of power big data and the demand analysis of Tianjin Electric Power Company to the business scene, and puts forward the user behavior analysis and visual display platform based on power big data. The platform realizes the function of customer classification and power consumption prediction, and visualizes the analysis results. Firstly, this paper analyzes the behavior, habits and rules of electricity consumption of the target customers. Combining data mining with customer classification, the whole model of power customer classification is designed and the index system is constructed. By combining clustering algorithm with Hadoop distributed processing framework, a parallel K-means algorithm based on Hadoop is presented. By analyzing the electricity consumption, credit and value creation of electric power customers, the function of customer classification is realized by using the marketing side data of power grid company. At the same time, the electricity consumption behavior of users has its own law. By combining association rule mining algorithm with Hadoop distributed processing framework, a parallel Apriori algorithm based on Hadoop is presented. By analyzing the influence and correlation of the related factors on the user's electricity consumption, the function of user's electricity consumption prediction is realized. Finally, the user's behavior is visualized through ExtJS technology. Assist the grid company to improve its own business level and management level, so that the grid company can carry out marketing business and improve its customer service ability.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP311.13;TM73

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