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Free Download Clustering Of Electricity Consumption Behavior Dynamics Toward Big Data Applications Project in Java Spring Framework with source code

Java Spring Framework project     kalai selvi    2020-01-05
  

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Clustering Of Electricity Consumption Behavior Dynamics Toward Big Data Applications project is a desktop application which is developed in Java Spring Framework platform. This Java Spring Framework project with tutorial and guide for developing a code. Clustering Of Electricity Consumption Behavior Dynamics Toward Big Data Applications is a open source you can Download zip and edit as per you need. If you want more latest Java Spring Framework projects here. This is simple and basic level small project for learning purpose. Also you can modified this system as per your requriments and develop a perfect advance level project. Zip file containing the source code that can be extracted and then imported into Eclipse. This Source code for BE, BTech, MCA, BCA, Engineering, Bs.CS, IT, Software Engineering final year students can submit in college. This script developed by kalai selvi. This desktop application 100% working smooth without any bug. It is developed using java,hadoop and Database hdfs. This software code helpful in academic projects for final year students. We have a great collection of Java Spring Framework projects.





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About Project



project Name :  Clustering Of Electricity Consumption Behavior Dynamics Toward Big Data Applications
project ID :  2437
Developer Name :  kalai selvi
Upload Date :  2020-01-05
project Platform :  Java Spring Framework
Programming Language :  java,hadoop
IDE Tool :  Eclipse
project Earning :  kalai selvi Earn Rs.0 from this project.
Database :  hdfs
project Type :  desktop Application
No of project Download :  77
project Total View :  1340
Today Trends :  1
Current Month Trends :  10
Last Month Trends :  50
project Source Code Link :   Download Here (6.2581MB)
project Report Link :  Download here  (5.6893MB)

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Features of the Project

In a competitive retail market, large volumes of smart meter data provide opportunities for load serving entities (LSEs) to enhance their knowledge of customers' electricity consumption behaviors via load profiling. Instead of focusing on the shape of the load curves, this paper proposes a novel approach for clustering of electricity consumption behavior dynamics, where “dynamics” refer to transitions and relations between consumption behaviors, or rather consumption levels, in adjacent periods. First, for each individual customer, symbolic aggregate approximation (SAX) is performed to reduce the scale of the data set, and time-based Markov model is applied to model the dynamic of electricity consumption, transforming the large data set of load curves to several state transition matrixes. Second, a clustering technique by Fast Search and Find of Density Peaks (CFSFDP) is primarily carried out to obtain the typical dynamics of consumption behavior, with the difference between any two consumption patterns measured by the Kullback–Liebler (K-L) distance, and to classify the customers into several clusters. To tackle the challenges of big data, the CFSFDP technique is integrated into a divide-and-conquer approach toward big data applications. A numerical case verifies the effectiveness of the proposed models and approaches.


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