Yunxuan Dong

775 total citations
20 papers, 546 citations indexed

About

Yunxuan Dong is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Management Science and Operations Research. According to data from OpenAlex, Yunxuan Dong has authored 20 papers receiving a total of 546 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Electrical and Electronic Engineering, 8 papers in Artificial Intelligence and 8 papers in Management Science and Operations Research. Recurrent topics in Yunxuan Dong's work include Energy Load and Power Forecasting (17 papers), Grey System Theory Applications (5 papers) and Stock Market Forecasting Methods (5 papers). Yunxuan Dong is often cited by papers focused on Energy Load and Power Forecasting (17 papers), Grey System Theory Applications (5 papers) and Stock Market Forecasting Methods (5 papers). Yunxuan Dong collaborates with scholars based in China, Macao and Australia. Yunxuan Dong's co-authors include Xuejiao Ma, Tonglin Fu, Xiao Ling, Yao Dong, Thomas Wu, Hongyu Zhu, Jianzhou Wang, Hui Hwang Goh, Dongdong Zhang and Shaodan Ma and has published in prestigious journals such as Applied Energy, Expert Systems with Applications and Energy Conversion and Management.

In The Last Decade

Yunxuan Dong

20 papers receiving 527 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Yunxuan Dong China	13	371	158	123	53	49	20	546
Sheng-Xiang Lv China	12	410 1.1×	248 1.6×	204 1.7×	57 1.1×	39 0.8×	14	697
M. Ghayekhloo Iran	10	485 1.3×	244 1.5×	118 1.0×	35 0.7×	49 1.0×	15	584
Zhewen Niu China	6	351 0.9×	168 1.1×	84 0.7×	41 0.8×	30 0.6×	15	492
Danxiang Wei Macao	12	356 1.0×	160 1.0×	116 0.9×	51 1.0×	26 0.5×	18	459
Zhihao Shang China	13	279 0.8×	162 1.0×	57 0.5×	62 1.2×	32 0.7×	28	480
Guangbiao Liu China	8	382 1.0×	96 0.6×	103 0.8×	24 0.5×	50 1.0×	11	510
J.-C. Peng China	6	435 1.2×	151 1.0×	66 0.5×	67 1.3×	25 0.5×	8	561
Hongze Li China	10	317 0.9×	181 1.1×	126 1.0×	24 0.5×	39 0.8×	19	674
Jiani Heng China	13	584 1.6×	253 1.6×	193 1.6×	109 2.1×	36 0.7×	25	740

Countries citing papers authored by Yunxuan Dong

Since Specialization

Citations

This map shows the geographic impact of Yunxuan Dong's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Yunxuan Dong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yunxuan Dong more than expected).

Fields of papers citing papers by Yunxuan Dong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Yunxuan Dong. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Yunxuan Dong. The network helps show where Yunxuan Dong may publish in the future.

Co-authorship network of co-authors of Yunxuan Dong

This figure shows the co-authorship network connecting the top 25 collaborators of Yunxuan Dong. A scholar is included among the top collaborators of Yunxuan Dong based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yunxuan Dong. Yunxuan Dong is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Dong, Yunxuan, Binggui Zhou, Hongcai Zhang, Guanghua Yang, & Shaodan Ma. (2025). A deep time–frequency augmented wind power forecasting model. Renewable Energy. 256. 123550–123550. 1 indexed citations

Dong, Yunxuan & Xiao Ling. (2024). Enhancing wind power generation prediction using relevance assessment-based transfer learning. Knowledge-Based Systems. 303. 112417–112417. 5 indexed citations

Dong, Yunxuan, et al.. (2024). A Hybrid Dual Stream ProbSparse Self-Attention Network for spatial–temporal photovoltaic power forecasting. Energy. 305. 132152–132152. 8 indexed citations

Dong, Yunxuan, et al.. (2023). A time series attention mechanism based model for tourism demand forecasting. Information Sciences. 628. 269–290. 26 indexed citations

Zhou, Binggui, et al.. (2023). A graph-attention based spatial-temporal learning framework for tourism demand forecasting. Knowledge-Based Systems. 263. 110275–110275. 16 indexed citations

Zhang, Dongdong, et al.. (2023). Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model. Energy. 285. 128762–128762. 87 indexed citations

Dong, Yunxuan, Binggui Zhou, Guanghua Yang, et al.. (2023). A novel model for tourism demand forecasting with spatial–temporal feature enhancement and image-driven method. Neurocomputing. 556. 126663–126663. 9 indexed citations

Wang, Zhongliang, Hongyu Zhu, Dongdong Zhang, et al.. (2023). Modelling of wind and photovoltaic power output considering dynamic spatio-temporal correlation. Applied Energy. 352. 121948–121948. 25 indexed citations

Wu, Thomas, Ruifeng Hu, Hongyu Zhu, et al.. (2023). Combined IXGBoost-KELM short-term photovoltaic power prediction model based on multidimensional similar day clustering and dual decomposition. Energy. 288. 129770–129770. 20 indexed citations

10.

Dong, Yunxuan, et al.. (2022). A Transfer Learning Based Deep Model for Electrical Load Prediction. 2251–2255. 1 indexed citations

11.

Dong, Yunxuan, Shaodan Ma, Hongcai Zhang, & Guanghua Yang. (2022). Wind Power Prediction Based on Multi-class Autoregressive Moving Average Model with Logistic Function. Journal of Modern Power Systems and Clean Energy. 10(5). 1184–1193. 36 indexed citations

12.

Dong, Yunxuan, Jing Wang, Xiao Ling, & Tonglin Fu. (2020). Short-term wind speed time series forecasting based on a hybrid method with multiple objective optimization for non-convex target. Energy. 215. 119180–119180. 18 indexed citations

13.

Dong, Yunxuan, Xuejiao Ma, & Tonglin Fu. (2020). Electrical load forecasting: A deep learning approach based on K-nearest neighbors. Applied Soft Computing. 99. 106900–106900. 102 indexed citations

14.

Ma, Xuejiao & Yunxuan Dong. (2020). An estimating combination method for interval forecasting of electrical load time series. Expert Systems with Applications. 158. 113498–113498. 24 indexed citations

15.

Ling, Xiao, Chen Wang, Yunxuan Dong, & Jianzhou Wang. (2019). A novel sub-models selection algorithm based on max-relevance and min-redundancy neighborhood mutual information. Information Sciences. 486. 310–339. 19 indexed citations

16.

Ling, Xiao, Yunxuan Dong, & Yao Dong. (2018). An improved combination approach based on Adaboost algorithm for wind speed time series forecasting. Energy Conversion and Management. 160. 273–288. 68 indexed citations

17.

Zhang, Kequan, Zongxi Qu, Yunxuan Dong, et al.. (2018). Research on a combined model based on linear and nonlinear features - A case study of wind speed forecasting. Renewable Energy. 130. 814–830. 52 indexed citations

18.

Dong, Yunxuan, Jianzhou Wang, & Zhenhai Guo. (2017). Research and application of local perceptron neural network in highway rectifier for time series forecasting. Applied Soft Computing. 64. 656–673. 9 indexed citations

19.

Wang, Jianzhou, Yunxuan Dong, Kequan Zhang, & Zhenhai Guo. (2017). A numerical model based on prior distribution fuzzy inference and neural networks. Renewable Energy. 112. 486–497. 7 indexed citations

20.

Dong, Yunxuan, Jianzhou Wang, Chen Wang, & Zhenhai Guo. (2017). Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting. Energies. 10(4). 490–490. 13 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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