Binrong Wu

747 total citations · 1 hit paper
22 papers, 521 citations indexed

About

Binrong Wu is a scholar working on Electrical and Electronic Engineering, Management Science and Operations Research and Economics and Econometrics. According to data from OpenAlex, Binrong Wu has authored 22 papers receiving a total of 521 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Electrical and Electronic Engineering, 10 papers in Management Science and Operations Research and 6 papers in Economics and Econometrics. Recurrent topics in Binrong Wu's work include Energy Load and Power Forecasting (14 papers), Stock Market Forecasting Methods (8 papers) and Market Dynamics and Volatility (5 papers). Binrong Wu is often cited by papers focused on Energy Load and Power Forecasting (14 papers), Stock Market Forecasting Methods (8 papers) and Market Dynamics and Volatility (5 papers). Binrong Wu collaborates with scholars based in China and United States. Binrong Wu's co-authors include Lin Wang, Yu‐Rong Zeng, Sirui Wang, Sheng-Xiang Lv, Lu Peng, Qing Zhu, Rui Tao, Jiacheng Lin, Haoyu Fang and Rui‐Hua Xu and has published in prestigious journals such as Applied Energy, Energy and Information Sciences.

In The Last Decade

Binrong Wu

19 papers receiving 510 citations

Hit Papers

Interpretable wind speed prediction with multivariate tim... 2022 2026 2023 2024 2022 50 100 150
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Interpretable wind speed prediction with multivariate time series and temporal fusion transformers
    2022 167 citations Binrong Wu, Lin Wang et al. Energy profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Binrong Wu China 11 247 147 137 122 47 22 521
Sheng-Xiang Lv China 12 410 1.7× 204 1.4× 93 0.7× 248 2.0× 49 1.0× 14 697
C. Narendra Babu India 12 262 1.1× 285 1.9× 84 0.6× 155 1.3× 75 1.6× 31 657
Slawek Smyl United States 8 300 1.2× 340 2.3× 61 0.4× 127 1.0× 65 1.4× 10 675
Mostafa Shabani Iran 6 131 0.5× 141 1.0× 42 0.3× 92 0.8× 46 1.0× 14 466
Wenjie Lu China 2 261 1.1× 401 2.7× 128 0.9× 131 1.1× 41 0.9× 4 695
Yuyang Gao China 12 368 1.5× 201 1.4× 45 0.3× 175 1.4× 65 1.4× 18 568
Yingchao Zou China 12 164 0.7× 177 1.2× 219 1.6× 43 0.4× 26 0.6× 33 412
Ce Li China 5 257 1.0× 101 0.7× 54 0.4× 121 1.0× 24 0.5× 8 420
Mehdi Khashei Iran 11 320 1.3× 448 3.0× 154 1.1× 177 1.5× 83 1.8× 19 755
Ratnadip Adhikari India 11 273 1.1× 333 2.3× 79 0.6× 161 1.3× 57 1.2× 14 592

Countries citing papers authored by Binrong Wu

Since Specialization
Citations

This map shows the geographic impact of Binrong Wu'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 Binrong Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Binrong Wu more than expected).

Fields of papers citing papers by Binrong Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Binrong Wu. 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 Binrong Wu. The network helps show where Binrong Wu may publish in the future.

Co-authorship network of co-authors of Binrong Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Binrong Wu. A scholar is included among the top collaborators of Binrong Wu 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 Binrong Wu. Binrong Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
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Wu, Binrong, et al.. (2025). Agricultural futures prices combination forecasting with whole-process interpretable method. Applied Soft Computing. 185. 114049–114049. 1 indexed citations
4.
Wu, Binrong, et al.. (2025). Interpretable wind speed forecasting through two-stage decomposition with comprehensive relative importance analysis. Applied Energy. 392. 126015–126015. 3 indexed citations
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Wu, Binrong, et al.. (2025). A novel data-driven model for explainable hog price forecasting. Applied Intelligence. 55(7). 1 indexed citations
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Wu, Binrong, et al.. (2024). Interpretable short-term carbon dioxide emissions forecasting based on flexible two-stage decomposition and temporal fusion transformers. Applied Soft Computing. 159. 111639–111639. 11 indexed citations
9.
Wu, Binrong, et al.. (2024). Interpretable corn future price forecasting with multivariate time series. Journal of Forecasting. 43(5). 1575–1594. 11 indexed citations
10.
Wu, Binrong, Lin Wang, & Yu‐Rong Zeng. (2024). Interpretable Tourism Demand Forecasting with Two-Stage Decomposition and Temporal Fusion Transformers. Journal of Systems Science and Complexity. 37(6). 2654–2679. 2 indexed citations
11.
Wu, Binrong, et al.. (2024). Interpretable wind speed forecasting with meteorological feature exploring and two-stage decomposition. Energy. 294. 130782–130782. 31 indexed citations
12.
Tao, Rui, Lin Wang, & Binrong Wu. (2023). A resource-efficient ECG diagnosis model for mobile health devices. Information Sciences. 648. 119628–119628. 10 indexed citations
13.
Wu, Binrong & Lin Wang. (2023). Two-stage decomposition and temporal fusion transformers for interpretable wind speed forecasting. Energy. 288. 129728–129728. 48 indexed citations
14.
Wu, Binrong, Lin Wang, & Yu‐Rong Zeng. (2022). Interpretable tourism demand forecasting with temporal fusion transformers amid COVID-19. Applied Intelligence. 53(11). 14493–14514. 24 indexed citations
15.
Lv, Xiujuan, Zheng Zheng, Xiao Zhi, et al.. (2022). Identification of RPGR ORF15 mutation for X-linked retinitis pigmentosa in a large Chinese family and in vitro correction with prime editor. Gene Therapy. 30(1-2). 160–166. 5 indexed citations
16.
Wu, Binrong, Lin Wang, Sheng-Xiang Lv, & Yu‐Rong Zeng. (2022). Forecasting oil consumption with attention-based IndRNN optimized by adaptive differential evolution. Applied Intelligence. 53(5). 5473–5496. 10 indexed citations
17.
Wu, Binrong, et al.. (2022). Interpretable tourism volume forecasting with multivariate time series under the impact of COVID-19. Neural Computing and Applications. 35(7). 5437–5463. 18 indexed citations
18.
Wu, Binrong, Lin Wang, & Yu‐Rong Zeng. (2022). Interpretable wind speed prediction with multivariate time series and temporal fusion transformers. Energy. 252. 123990–123990. 167 indexed citations breakdown →
19.
Wu, Binrong, Lin Wang, Sirui Wang, & Yu‐Rong Zeng. (2021). Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic. Energy. 226. 120403–120403. 86 indexed citations
20.
Wang, Lin, Binrong Wu, Qing Zhu, & Yu‐Rong Zeng. (2020). Forecasting Monthly Tourism Demand Using Enhanced Backpropagation Neural Network. Neural Processing Letters. 52(3). 2607–2636. 26 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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