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Design And Development Of Orderly Electricity Management System Based On Hadoop

Posted on:2019-12-29Degree:MasterType:Thesis
Country:ChinaCandidate:Q PengFull Text:PDF
GTID:2428330548485702Subject:Engineering
Abstract/Summary:PDF Full Text Request
In recent years,our country vigorously promotes the implementation of power demand-side management,the orderly electricity management is one of the important links.The traditional supervise way of orderly electricity is rigid,there are not consider users interests.How to fully take into account the interests of users,scientific and meticulous formulation of an orderly electricity policy,and improve user's participation and responsiveness of orderly electricity management,it is a dilemma that has plagued researchers for so long.This paper collected user's electricity data and social electricity data based on the SG186 and SG-ERP project construction of SGCC,designed and developed a complete Hadoop-based orderly electricity management system based on Internet and data mining analysis technology.This system aim at maintain the interests of users and improve the experience and enthusiam when users participating the orderly electricity.The system is divided into load forecasting module and orderly electricity management module.In the load forecasting module,the city's electricity load curve shows the daily load characteristics and predicts the future short-term load characteristics with simple line charts and map models.This module use means of linear regression establish a short-term power load model to forecast power load which in a short period of time.In the orderly electricity management module,this system implement select data by map mode through the data visualization technology,at the same time,this module also can select the user's orderly electricity daily report and orderly electricity program in detail.In order to improve the efficiency of clustering users' electrical characteristics,this paper combines Canpoy clustering algorithm and K-means clustering algorithm,calculate the number of clusters based on user's historical daily load data using Canopy algorithm,then use K-means algorithm clustering four types of typical user electricity patterns.On the overall system structure,system based on Hadoop distributed architecture,and the system database combina the Hive database and Mysql database to improve the system's ability to cope with concurrent calculations and operations.
Keywords/Search Tags:Orderly electricity, Linear regression, Load Forecasting, Canopy algorithm, K-means algorithm
PDF Full Text Request
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