World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
48
Citations
6976
World Ranking
6261
National Ranking
836

Chaoshun Li publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Chaoshun Li sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 128 publications — 18th percentile

18% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Chaoshun Li D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Chaoshun Li sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 48 D-Index — 58th percentile

58% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Chaoshun Li is affiliated with the Huazhong University of Science and Technology in China, specializing in engineering disciplines. Their research primarily focuses on electrical and electronic engineering, control and systems engineering, mechanics of materials, civil and structural engineering, and artificial intelligence.

Their numerous publications contribute significantly to fields such as energy load and power forecasting, machine fault diagnosis techniques, power system optimization and stability, cavitation phenomena in pumps, electric power system optimization, water systems and optimization, and microgrid control and optimization.

Frequent publication venues for their work include:

  • Energy
  • Renewable Energy
  • SSRN Electronic Journal
  • IEEE Access
  • Journal of Energy Storage

Notable recent papers authored by Chaoshun Li include:

  • The short-term interval prediction of wind power using the deep learning model with gradient descend optimization, 2020, Renewable Energy

Other significant papers from related research include:

  • Temporal convolutional networks interval prediction model for wind speed forecasting, 2020, Electric Power Systems Research
  • EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning, 2020, Energy
  • A Novel Wind Speed Interval Prediction Based on Error Prediction Method, 2020, IEEE Transactions on Industrial Informatics
  • A novel fault diagnosis procedure based on improved symplectic geometry mode decomposition and optimized SVM, 2020, Measurement

Chaoshun Li collaborates frequently with several coauthors, including:

  • Xueding Lu (18 joint publications)
  • Xiaoqiang Tan (16 joint publications)
  • Zhiwei Zhu (14 joint publications)
  • Dong Liu (13 joint publications)
  • Jie Huang (11 joint publications)

Their research integrates deep learning models and optimization techniques applied to wind power and energy forecasting, emphasizing interval prediction methodologies. The work typically intersects machine learning approaches with power system applications, addressing both theoretical and practical challenges in renewable energy forecasting and system stability.

Best Publications

  • Parameters identification of hydraulic turbine governing system using improved gravitational search algorithm

    Chaoshun Li;Jianzhong Zhou

  • Short-Term Wind Speed Interval Prediction Based on Ensemble GRU Model

    Chaoshun Li;Geng Tang;Xiaoming Xue;Adnan Saeed

  • A compound structure of ELM based on feature selection and parameter optimization using hybrid backtracking search algorithm for wind speed forecasting

    Chu Zhang;Jianzhong Zhou;Chaoshun Li;Wenlong Fu

  • Design of a fractional-order PID controller for a pumped storage unit using a gravitational search algorithm based on the Cauchy and Gaussian mutation

    Chaoshun Li;Nan Zhang;Xinjie Lai;Jianzhong Zhou

  • Deep Learning Method Based on Gated Recurrent Unit and Variational Mode Decomposition for Short-Term Wind Power Interval Prediction

    Ruoheng Wang;Chaoshun Li;Wenlong Fu;Geng Tang

  • Load Frequency Control of a Novel Renewable Energy Integrated Micro-Grid Containing Pumped Hydropower Energy Storage

    Yanhe Xu;Chaoshun Li;Zanbin Wang;Nan Zhang

  • Multi-step short-term wind speed forecasting approach based on multi-scale dominant ingredient chaotic analysis, improved hybrid GWO-SCA optimization and ELM

    Wenlong Fu;Kai Wang;Chaoshun Li;Jiawen Tan

  • Temporal convolutional networks interval prediction model for wind speed forecasting

    Zhenhao Gan;Chaoshun Li;Jianzhong Zhou;Geng Tang

  • An adaptively fast fuzzy fractional order PID control for pumped storage hydro unit using improved gravitational search algorithm

    Yanhe Xu;Jianzhong Zhou;Xiaoming Xue;Wenlong Fu

  • T–S Fuzzy Model Identification With a Gravitational Search-Based Hyperplane Clustering Algorithm

    Chaoshun Li;Jianzhong Zhou;Bo Fu;Pangao Kou

  • A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting

    Chaoshun Li;Zhengguang Xiao;Xin Xia;Wen Zou

  • An adaptively fast ensemble empirical mode decomposition method and its applications to rolling element bearing fault diagnosis

    Xiaoming Xue;Jianzhong Zhou;Yanhe Xu;Wenlong Zhu

  • T-S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm

    Chaoshun Li;Jianzhong Zhou;Xiuqiao Xiang;Qingqing Li

  • Study on unit commitment problem considering pumped storage and renewable energy via a novel binary artificial sheep algorithm

    Wenxiao Wang;Chaoshun Li;Xiang Liao;Hui Qin

  • Compound feature selection and parameter optimization of ELM for fault diagnosis of rolling element bearings.

    Meng Luo;Chaoshun Li;Xiaoyuan Zhang;Ruhai Li

  • EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning

    Xiaosheng Peng;Hongyu Wang;Jianxun Lang;Wenze Li

  • A novel chaotic particle swarm optimization based fuzzy clustering algorithm

    Chaoshun Li;Jianzhong Zhou;Pangao Kou;Jian Xiao

  • Parameters identification of chaotic system by chaotic gravitational search algorithm

    Chaoshun Li;Jianzhong Zhou;Jian Xiao;Han Xiao

  • Adaptive condition predictive-fuzzy PID optimal control of start-up process for pumped storage unit at low head area

    Yanhe Xu;Yang Zheng;Yi Du;Wen Yang

  • The short-term interval prediction of wind power using the deep learning model with gradient descend optimization

    Chaoshun Li;Geng Tang;Xiaoming Xue;Xinbiao Chen

  • Multi-objective complementary scheduling of hydro-thermal-RE power system via a multi-objective hybrid grey wolf optimizer

    Chaoshun Li;Wenxiao Wang;Deshu Chen

Frequent Co-Authors

Jianzhong Zhou
Jianzhong Zhou Huazhong University of Science and Technology
Diyi Chen
Diyi Chen Northwest A&F University
Shanxu Duan
Shanxu Duan Huazhong University of Science and Technology
Om P. Malik
Om P. Malik University of Calgary
Pak Kin Wong
Pak Kin Wong University of Macau

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