A Reconstruction Method for Missing Data of electricity users Using Extremely Randomized Tree-Reference-Cited by-同舟云学术

A Reconstruction Method for Missing Data of electricity users Using Extremely Randomized Tree

Published:2021-11 Issue: Volume: Page:
ISSN:
Container-title:2021 International Conference on Computer, Internet of Things and Control Engineering (CITCE)
language:
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Author:

Liu Ao¹,Li Fusheng²,Li Jiangnan¹,Qian Bin³,Che Yiying¹,Zhou Mi³

Affiliation:

1. Shenzhen Power Supply Bureau Co. Ltd,Shenzhen,China

2. Electric Power Research Institute, CSG Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid,Guangzhou,China

3. Electric Power Research Institute, CSG Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid,Guangzhou,China

Funder

China Southern Power Grid

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9723820/9723600/09723608.pdf?arnumber=9723608

Reference11 articles.

1. Missing data in experiments: Challenges and solutions.

2. Methodolgical Progress Note: Handling Missing Data in Clinical Research

3. A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities

4. Monitoring Method of Wide-band Oscillation Based on Compressed Sensing of Diagonalizable LDPC and k-nearest Neighbour Algorithm;feng;Power System Technology,2021

5. A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models

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