An XGBoost Ensemble Model for Residential Load Forecasting-Reference-Cited by-同舟云学术

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An XGBoost Ensemble Model for Residential Load Forecasting

Published:2021 Issue: Volume: Page:321-334
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
Short-container-title:

Author:

Venkat Karthik,Gautam Tarika,Yadav Mohit,Singh Mukhtiar

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-15-8443-5_26

Reference20 articles.

1. Marcincin, O., Medvec, Z., Moldrik, P.: The impact of electric vehicles on distribution network. In: 2017 18th International Scientific Conference on Electric Power Engineering (EPE), Kouty nad Desnou, 2017, pp. 1–5, doi: https://doi.org/10.1109/EPE.2017.7967344

2. Senjyu, T., Takara, H., Uezato, K., Funabashi, T., Member, S. : One-Hour-Ahead Load Forecasting Using Neural Network (2002)

3. Yu, C.N., Mirowski, P., Ho, T.K.: A sparse coding approach to household electricity demand forecasting in smart grids. IEEE Trans. Smart Grid 8(2), 738–748 (2017). https://doi.org/10.1109/TSG.2015.2513900

4. Rashed Mohassel, R., Fung, A., Mohammadi, F., Raahemifar, K.: A survey on advanced metering infrastructure. Int. J. Electr. Power Energy Syst. 63, 473–484 (2014). doi: https://doi.org/10.1016/j.ijepes.2014.06.025

5. Wu, D., Wang, B., Precup, D., Boulet, B.: Multiple Kernel learning-based transfer regression for electric load forecasting. IEEE Trans. Smart Grid 11(2), 1183–1192 (2020). https://doi.org/10.1109/TSG.2019.2933413

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