A Robust Approach For Data Cleaning Used By Decision Tree Induction Method In The Enterprise Data Warehouse

Dara, Raju

doi:10.5121/ijcsa.2015.5406

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Using an existing 10 kV network based on distribution phenomenon as an example, the best electrical condition indicators in this paper are monthly active power supply, public transformer monthly active power supply, monthly reactive power supply, total capacity of the public transformer, and number of public sets with the most extended compact length [16]. ese usual electrical indications are considered the AGA-BPNN input, and the losses in line of the 10 kV network based on distribution phenomenon are considered the AGA-BPNN output [17,18].…”

Section: Electrical Characteristic Indexmentioning

confidence: 99%

Predicting the Line Loss for a 10 kV Distribution Network Using AGA-BPNN

Zhao

2022

Mobile Information Systems

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With the rapid development of power grids, it is essential for these grid enterprises to pay more and more attention to the comprehensive reduction of power loss. Due to a large number of medium voltage power network based on distribution phenomenon nodes and power loss, it is difficult to collect accurate data of more than 20% of the total power loss of the network based on distribution phenomenon. Based on the above problems, it is significant to propose a method to quickly and accurately predict the losses in line of the 10 kV network based on distribution phenomenon. The improved BP neural network model and adaptive evolutionary algorithm programming can effectively analyze the 10kV transmission and distribution problems and reduce the line loss in the transmission project. The line loss prediction model mainly includes data cleaning, electrical characteristic index system, determination of the number of nodes in the BPNN hidden layer, and losses in line prediction. For this purpose, AGA-BPNN is proposed in this paper. Additionally, cable quality, power factor management, and reduction resistance are some parameters that can help lower the losses in lines. The author studies the performance of the losses in the line prediction model before and after the improvement of BPNN to validate the application impact of the AGA-BPNN algorithm in losses in line prediction of a 10 kV network based on distribution phenomenon. This technique has benefits of rapid convergence and great accuracy over ordinary BPNN. The simulation and computation of the example validate the suggested approach.

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Section: Electrical Characteristic Indexmentioning

confidence: 99%

Predicting the Line Loss for a 10 kV Distribution Network Using AGA-BPNN

Zhao

2022

Mobile Information Systems

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A Novel Approach for Data Cleaning by Selecting the Optimal Data to Fill the Missing Values for Maintaining Reliable Data Warehouse

Dara¹,

Satyanarayana²,

Govardhan³

2016

IJMECS

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Abstract-At present trillion of bytes of information is being created by projects particularly in web. To accomplish the best choice for business benefits, access to that information in a very much arranged and intuitive way is dependably a fantasy of business administrators and chiefs. Information warehouse is the main feasible arrangement that can bring the fantasy into reality. The upgrade of future attempts to settle on choices relies on upon the accessibility of right data that depends on nature of information basic. The quality information must be created by cleaning information preceding stacking into information distribution center following the information gathered from diverse sources will be grimy. Once the information have been pre-prepared and purified then it produces exact results on applying the information mining question. There are numerous cases where the data is sparse in nature. To get accurate results with sparse data is hard. In this paper the main goal is to fill the missing values in acquired data which is sparse in nature. Precisely caution must be taken to choose minimum number of text pieces to fill the holes for which we have used Jaccard Dissimilarity function for clustering the data which is frequent in nature.

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A Robust Approach For Data Cleaning Used By Decision Tree Induction Method In The Enterprise Data Warehouse

Cited by 2 publications

References 6 publications

Predicting the Line Loss for a 10 kV Distribution Network Using AGA-BPNN

Predicting the Line Loss for a 10 kV Distribution Network Using AGA-BPNN

A Novel Approach for Data Cleaning by Selecting the Optimal Data to Fill the Missing Values for Maintaining Reliable Data Warehouse

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