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Cited by in F6Publishing
For: Karakuş O, Kuruoğlu EE, Altınkaya MA. One‐day ahead wind speed/power prediction based on polynomial autoregressive model. IET Renewable Power Generation 2017;11:1430-9. [DOI: 10.1049/iet-rpg.2016.0972] [Cited by in Crossref: 54] [Cited by in F6Publishing: 4] [Article Influence: 10.8] [Reference Citation Analysis]
Number Citing Articles
1 Wang J, Wang S, Yang W. A novel non-linear combination system for short-term wind speed forecast. Renewable Energy 2019;143:1172-92. [DOI: 10.1016/j.renene.2019.04.154] [Cited by in Crossref: 23] [Cited by in F6Publishing: 3] [Article Influence: 7.7] [Reference Citation Analysis]
2 Santhosh M, Venkaiah C, Kumar DV. Short-term wind speed forecasting approach using Ensemble Empirical Mode Decomposition and Deep Boltzmann Machine. Sustainable Energy, Grids and Networks 2019;19:100242. [DOI: 10.1016/j.segan.2019.100242] [Cited by in Crossref: 29] [Article Influence: 9.7] [Reference Citation Analysis]
3 Li C, Lin S, Xu F, Liu D, Liu J. Short-term wind power prediction based on data mining technology and improved support vector machine method: A case study in Northwest China. Journal of Cleaner Production 2018;205:909-22. [DOI: 10.1016/j.jclepro.2018.09.143] [Cited by in Crossref: 60] [Cited by in F6Publishing: 13] [Article Influence: 15.0] [Reference Citation Analysis]
4 Ding M, Zhou H, Xie H, Wu M, Liu KZ, Nakanishi Y, Yokoyama R. A time series model based on hybrid-kernel least-squares support vector machine for short-term wind power forecasting. ISA Trans 2021;108:58-68. [PMID: 32958296 DOI: 10.1016/j.isatra.2020.09.002] [Cited by in Crossref: 9] [Article Influence: 4.5] [Reference Citation Analysis]
5 Li L, Zhao X, Tseng M, Tan RR. Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm. Journal of Cleaner Production 2020;242:118447. [DOI: 10.1016/j.jclepro.2019.118447] [Cited by in Crossref: 85] [Cited by in F6Publishing: 11] [Article Influence: 42.5] [Reference Citation Analysis]
6 Li L, Chang Y, Tseng M, Liu J, Lim MK. Wind power prediction using a novel model on wavelet decomposition-support vector machines-improved atomic search algorithm. Journal of Cleaner Production 2020;270:121817. [DOI: 10.1016/j.jclepro.2020.121817] [Cited by in Crossref: 14] [Cited by in F6Publishing: 2] [Article Influence: 7.0] [Reference Citation Analysis]
7 Fu W, Wang K, Li C, Tan J. Multi-step short-term wind speed forecasting approach based on multi-scale dominant ingredient chaotic analysis, improved hybrid GWO-SCA optimization and ELM. Energy Conversion and Management 2019;187:356-77. [DOI: 10.1016/j.enconman.2019.02.086] [Cited by in Crossref: 81] [Cited by in F6Publishing: 5] [Article Influence: 27.0] [Reference Citation Analysis]