Fuzzy support vector regressions for short-term load forecasting-Reference-Cited by-同舟云学术

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Fuzzy support vector regressions for short-term load forecasting

Published:2024-06-14 Issue:3 Volume:23 Page:363-385
ISSN:1568-4539
Container-title:Fuzzy Optimization and Decision Making
language:en
Short-container-title:Fuzzy Optim Decis Making

Author:

Luo Jian,Zheng Yukai,Hong Tao,Luo An,Yang Xueqi

Funder

National Natural Science Foundation of China

Humanities and Social Sciences Youth Foundation, Ministry of Education of the People's Republic of China

‘Nanhai New Star’ Philosophy and Social Science Talent Project of Hainan Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10700-024-09425-x.pdf

Reference23 articles.

1. Chen, B.-J., Chang, M.-W., & Lin, C.-J. (2004). Load forecasting using support vector machines: A study on EUNITE competition 2001. IEEE Transactions on Power Systems, 19(4), 1821–1830.

2. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297.

3. Eskandarpour, R., & Khodaei, A. (2019). Probabilistic load curtailment estimation using posterior probability model and twin support vector machine. Journal of Modern Power System and Clean Energy, 7(4), 665–675.

4. Ghadim, H. V., Hagh, M. T., & Zadeh, S. G. (2023). Fermat-curve based fuzzy inference system for the fuzzy logic controller performance optimization in load frequency control application. Fuzzy Optimization and Decision Making, 22, 555–586.

5. Hong, T., & Fan, S. (2016). Probabilistic electric load forecasting: A tutorial review. International Journal of Forecasting, 32(3), 914–938.

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