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Cited by in F6Publishing
For: 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]
Number Citing Articles
1 Wu B, Wang L, Zeng Y. Interpretable wind speed prediction with multivariate time series and temporal fusion transformers. Energy 2022;252:123990. [DOI: 10.1016/j.energy.2022.123990] [Reference Citation Analysis]
2 Liu H, Li Y, Duan Z, Chen C. A review on multi-objective optimization framework in wind energy forecasting techniques and applications. Energy Conversion and Management 2020;224:113324. [DOI: 10.1016/j.enconman.2020.113324] [Cited by in Crossref: 20] [Cited by in F6Publishing: 1] [Article Influence: 10.0] [Reference Citation Analysis]
3 Li R, Hu Y, Heng J, Chen X. A novel multiscale forecasting model for crude oil price time series. Technological Forecasting and Social Change 2021;173:121181. [DOI: 10.1016/j.techfore.2021.121181] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Liu H, Duan Z, Chen C. Wind speed big data forecasting using time-variant multi-resolution ensemble model with clustering auto-encoder. Applied Energy 2020;280:115975. [DOI: 10.1016/j.apenergy.2020.115975] [Cited by in Crossref: 11] [Cited by in F6Publishing: 2] [Article Influence: 5.5] [Reference Citation Analysis]
5 Liu X, Zhou J, Qian H. Short-term wind power forecasting by stacked recurrent neural networks with parametric sine activation function. Electric Power Systems Research 2021;192:107011. [DOI: 10.1016/j.epsr.2020.107011] [Cited by in Crossref: 9] [Cited by in F6Publishing: 5] [Article Influence: 9.0] [Reference Citation Analysis]
6 Zhang S, Wang C, Liao P, Xiao L, Fu T. Wind speed forecasting based on model selection, fuzzy cluster, and multi-objective algorithm and wind energy simulation by Betz's theory. Expert Systems with Applications 2022;193:116509. [DOI: 10.1016/j.eswa.2022.116509] [Reference Citation Analysis]
7 Li H. Short-Term Wind Power Prediction via Spatial Temporal Analysis and Deep Residual Networks. Front Energy Res 2022;10:920407. [DOI: 10.3389/fenrg.2022.920407] [Reference Citation Analysis]
8 Hu J, Heng J, Wen J, Zhao W. Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm. Renewable Energy 2020;162:1208-26. [DOI: 10.1016/j.renene.2020.08.077] [Cited by in Crossref: 11] [Cited by in F6Publishing: 2] [Article Influence: 5.5] [Reference Citation Analysis]
9 Liu C, Zhang X, Mei S, Zhen Z, Jia M, Li Z, Tang H. Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness. Applied Energy 2022;313:118769. [DOI: 10.1016/j.apenergy.2022.118769] [Reference Citation Analysis]
10 Guo X, Zhu C, Hao J, Zhang S, Zhu L. A hybrid method for short-term wind speed forecasting based on Bayesian optimization and error correction. Journal of Renewable and Sustainable Energy 2021;13:036101. [DOI: 10.1063/5.0048686] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 Yu Y, Wang J, Liu Z, Zhao W. A combined forecasting strategy for the improvement of operational efficiency in wind farm. Journal of Renewable and Sustainable Energy 2021;13:063310. [DOI: 10.1063/5.0065937] [Reference Citation Analysis]
12 İnaç T, Dokur E, Yüzgeç U. A multi-strategy random weighted gray wolf optimizer-based multi-layer perceptron model for short-term wind speed forecasting. Neural Comput & Applic. [DOI: 10.1007/s00521-022-07303-4] [Reference Citation Analysis]
13 Shao Y, Wang J, Zhang H, Zhao W. An advanced weighted system based on swarm intelligence optimization for wind speed prediction. Applied Mathematical Modelling 2021;100:780-804. [DOI: 10.1016/j.apm.2021.07.024] [Reference Citation Analysis]