Non-Intrusive Load Monitoring Based on Feature Extraction of Change-point and XGBoost Classifier-Reference-Cited by-同舟云学术

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Non-Intrusive Load Monitoring Based on Feature Extraction of Change-point and XGBoost Classifier

Published:2020-10-30 Issue: Volume: Page:
ISSN:
Container-title:2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2)
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Author:

Chen Zhuo¹,Chen Junxingxu¹,Xu Xianyong¹,Peng Shuangjian¹,Xiao Jian¹,Qiao Hong¹

Affiliation:

1. Company Limited Research Institute,State Grid Hunan Electric Power,Changsha,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9346557/9346568/09347014.pdf?arnumber=9347014

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Comparing four machine learning algorithms for household non-intrusive load monitoring;Energy and AI;2024-09

2. Short-Term Residental DC Load Forecasting Using Extreme Gradient Boost (XgBoost) Algorithm;2024 IEEE 18th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG);2024-06-24

3. Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring;Electronics;2024-04-09

4. Analysis of safe electricity consumption on load side based on attack and defence game model;The Journal of Engineering;2024-04

5. Comparative Analysis of Machine Learning Techniques for Indoor Illuminance Prediction and Building Energy Efficiency: Innovative Approaches for Efficient Daylight Linked Control Systems;2024

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