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Cited by in F6Publishing
For: 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]
Number Citing Articles
1 Chen J, Ma S, Wu Y. International carbon financial market prediction using particle swarm optimization and support vector machine. J Ambient Intell Human Comput. [DOI: 10.1007/s12652-021-03240-7] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
2 Xu Y, Long Z, Pan W, Wang Y. Low-cost sensor outlier detection framework for on-line monitoring of particle pollutants in multiple scenarios. Environ Sci Pollut Res Int 2021. [PMID: 34021450 DOI: 10.1007/s11356-021-14419-y] [Reference Citation Analysis]
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4 Izci D. A novel improved atom search optimization algorithm for designing power system stabilizer. Evol Intel . [DOI: 10.1007/s12065-021-00615-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
5 Zhou J, Xu Z, Wang S. A novel dual-scale ensemble learning paradigm with error correction for predicting daily ozone concentration based on multi-decomposition process and intelligent algorithm optimization, and its application in heavily polluted regions of China. Atmospheric Pollution Research 2022;13:101306. [DOI: 10.1016/j.apr.2021.101306] [Reference Citation Analysis]
6 An Y, Wang J, Lu H, Zhao W. Research of a combined wind speed model based on multi‐objective ant lion optimization algorithm. Int Trans Electr Energ Syst 2021;31. [DOI: 10.1002/2050-7038.13189] [Reference Citation Analysis]
7 Yildiz C, Acikgoz H, Korkmaz D, Budak U. An improved residual-based convolutional neural network for very short-term wind power forecasting. Energy Conversion and Management 2021;228:113731. [DOI: 10.1016/j.enconman.2020.113731] [Cited by in Crossref: 17] [Cited by in F6Publishing: 1] [Article Influence: 17.0] [Reference Citation Analysis]
8 Liu B, Song C, Wang Q, Zhang X, Chen J. Research on regional differences of China's new energy vehicles promotion policies: A perspective of sales volume forecasting. Energy 2022;248:123541. [DOI: 10.1016/j.energy.2022.123541] [Reference Citation Analysis]
9 Goh HH, He R, Zhang D, Liu H, Dai W, Lim CS, Kurniawan TA, Teo KTK, Goh KC. A multimodal approach to chaotic renewable energy prediction using meteorological and historical information. Applied Soft Computing 2022;118:108487. [DOI: 10.1016/j.asoc.2022.108487] [Reference Citation Analysis]
10 Wang J, Gao D, Zhuang Z, Wu J. An optimized complementary prediction method based on data feature extraction for wind speed forecasting. Sustainable Energy Technologies and Assessments 2022;52:102068. [DOI: 10.1016/j.seta.2022.102068] [Reference Citation Analysis]
11 Zhang Y, Mao S, Kang Y. A clean energy forecasting model based on artificial intelligence and fractional derivative grey Bernoulli models. GS 2020;11:571-95. [DOI: 10.1108/gs-08-2020-0101] [Cited by in Crossref: 4] [Article Influence: 2.0] [Reference Citation Analysis]