XgBoost based Short-term Electrical Load Forecasting Considering Trends & Periodicity in Historical Data-Reference-Cited by-同舟云学术

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XgBoost based Short-term Electrical Load Forecasting Considering Trends & Periodicity in Historical Data

Published:2023-12-03 Issue: Volume:8 Page:1-6
ISSN:
Container-title:2023 IEEE International Conference on Energy Technologies for Future Grids (ETFG)
language:
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Author:

Shabbir Noman¹,Ahmadiahangar Roya¹,Rosin Argo¹,Jawad Muhammad²,Kilter Jako³,Martins Joao⁴

Affiliation:

1. Tallinn University of Technology,FinEST Center for Smart Cities,Tallinn,Estonia

2. COMSATS University,Department of Electrical & Computer Engineering,Lahore,Pakistan

3. Tallinn University of Technology,Department of Electrical Power Engineering,Tallinn,Estonia

4. NOVA University,Department of Electrical & Computer Engineering,Lisbon,Portugal

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10406302/10407105/10407926.pdf?arnumber=10407926

Reference25 articles.

1. Residential Load Forecasting Using Recurrent Neural Networks

2. A Kalman filter-based bottom-up approach for household short-term load forecast

3. Combined PV–EV hosting capacity assessment for a residential LV distribution grid with smart EV charging and PV curtailment

4. Exploratory Data Analysis Based Short-Term Electrical Load Forecasting: A Comprehensive Analysis

5. Integrated Forecasting Method for Wind Energy: ACase Study in China;Case;Processes,2020

Cited by 1 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. Comparative Analysis of Machine Learning Techniques for Non-Intrusive Load Monitoring;Electronics;2024-04-09

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