World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
64
Citations
12338
World Ranking
1697
National Ranking
335

Weihao Hu publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Weihao Hu sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 395 publications — 89th percentile

89% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Weihao Hu D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Weihao Hu sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 64 D-Index — 84th percentile

84% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Weihao Hu is affiliated with the University of Electronic Science and Technology of China, located in China. Their research primarily focuses on the field of Engineering, with substantial contributions to Electrical and Electronic Engineering, Control and Systems Engineering, and Energy Engineering and Power Technology.

The scientist's work spans several subfields, including Electrical and Electronic Engineering, Control and Systems Engineering, Energy Engineering and Power Technology, Mechanical Engineering, and Automotive Engineering. Their research addresses a variety of topics such as Smart Grid Energy Management, Microgrid Control and Optimization, Optimal Power Flow Distribution, Integrated Energy Systems Optimization, Energy Load and Power Forecasting, Hybrid Renewable Energy Systems, and Electric Vehicles and Infrastructure.

Weihao Hu has contributed numerous publications across a variety of respected venues in the energy and power domain. Frequent publication venues include:

  • Renewable Energy
  • Applied Energy
  • IEEE Transactions on Power Systems
  • Energy Reports
  • SSRN Electronic Journal

Their recent papers feature collaborations with co-authors such as Zhe Chen, Qi Huang, Di Cao, Frede Blaabjerg, and Zhenyuan Zhang. Notable recent publications include:

  • Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review, 2020, Journal of Modern Power Systems and Clean Energy
  • A Novel Hybrid Short-Term Load Forecasting Method of Smart Grid Using MLR and LSTM Neural Network, 2020, IEEE Transactions on Industrial Informatics
  • A Multi-Agent Deep Reinforcement Learning Based Voltage Regulation Using Coordinated PV Inverters, 2020, IEEE Transactions on Power Systems
  • Data-Driven Multi-Agent Deep Reinforcement Learning for Distribution System Decentralized Voltage Control With High Penetration of PVs, 2021, IEEE Transactions on Smart Grid
  • Deep Reinforcement Learning Enabled Physical-Model-Free Two-Timescale Voltage Control Method for Active Distribution Systems, 2021, IEEE Transactions on Smart Grid

In addition to journal articles, Weihao Hu has published work in book form. One of the books listed is Planning and Operation of Hybrid Renewable Energy Systems, published by Frontiers Media in 2022.

Best Publications

  • Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

    Di Cao;Weihao Hu;Junbo Zhao;Guozhou Zhang

  • Optimized sizing of a standalone PV-wind-hydropower station with pumped-storage installation hybrid energy system

    Xiao Xu;Weihao Hu;Di Cao;Qi Huang

  • A Novel Hybrid Short-Term Load Forecasting Method of Smart Grid Using MLR and LSTM Neural Network

    Jian Li;Daiyu Deng;Junbo Zhao;Dongsheng Cai

  • A Multi-Agent Deep Reinforcement Learning Based Voltage Regulation Using Coordinated PV Inverters

    Di Cao;Weihao Hu;Junbo Zhao;Qi Huang

  • Optimizing investments in coupled offshore wind -electrolytic hydrogen storage systems in Denmark

    Peng Hou;Peter Enevoldsen;Joshua Eichman;Weihao Hu

  • A Heuristic Planning Reinforcement Learning-Based Energy Management for Power-Split Plug-in Hybrid Electric Vehicles

    Teng Liu;Xiaosong Hu;Weihao Hu;Yuan Zou

  • Optimized Placement of Wind Turbines in Large-Scale Offshore Wind Farm Using Particle Swarm Optimization Algorithm

    Peng Hou;Weihao Hu;Mohsen Soltani;Zhe Chen

  • Investigation of wind speed cooling effect on PV panels in windy locations

    Nuri Gökmen;Weihao Hu;Peng Hou;Zhe Chen

  • Flicker Mitigation by Active Power Control of Variable-Speed Wind Turbines With Full-Scale Back-to-Back Power Converters

    Weihao Hu;Zhe Chen;Yue Wang;Zhaoan Wang

  • A Meta-Learning Method for Electric Machine Bearing Fault Diagnosis Under Varying Working Conditions With Limited Data

    Unknown

  • Optimal Operation of Plug-In Electric Vehicles in Power Systems With High Wind Power Penetrations

    Weihao Hu;Chi Su;Zhe Chen;Birgitte Bak-Jensen

  • Data-Driven Multi-Agent Deep Reinforcement Learning for Distribution System Decentralized Voltage Control With High Penetration of PVs

    Di Cao;Junbo Zhao;Weihao Hu;Fei Ding

  • A review of offshore wind farm layout optimization and electrical system design methods

    Peng Hou;Jiangsheng Zhu;Kuichao Ma;Guangya Yang

  • Optimal operation strategy of battery energy storage system to real-time electricity price in Denmark

    Weihao Hu;Zhe Chen;Birgitte Bak-Jensen

  • Deep Reinforcement Learning Enabled Physical-Model-Free Two-Timescale Voltage Control Method for Active Distribution Systems

    Di Cao;Junbo Zhao;Weihao Hu;Nanpeng Yu

  • Attention Enabled Multi-Agent DRL for Decentralized Volt-VAR Control of Active Distribution System Using PV Inverters and SVCs

    Di Cao;Junbo Zhao;Weihao Hu;Fei Ding

  • Deep reinforcement learning–based approach for optimizing energy conversion in integrated electrical and heating system with renewable energy

    Bin Zhang;Weihao Hu;Di Cao;Qi Huang

  • Soft actor-critic –based multi-objective optimized energy conversion and management strategy for integrated energy systems with renewable energy

    Bin Zhang;Weihao Hu;Di Cao;Tao Li

  • Reinforcement Learning Based Efficiency Optimization Scheme for the DAB DC–DC Converter With Triple-Phase-Shift Modulation

    Yuanhong Tang;Weihao Hu;Jian Xiao;Zhangyong Chen

  • Dynamic energy conversion and management strategy for an integrated electricity and natural gas system with renewable energy: Deep reinforcement learning approach

    Bin Zhang;Weihao Hu;Jinghua Li;Di Cao

  • Electric vehicles and large-scale integration of wind power – The case of Inner Mongolia in China

    Wen Liu;Weihao Hu;Henrik Lund;Zhe Chen

  • Combined optimization for offshore wind turbine micro siting

    Peng Hou;Weihao Hu;Mohsen N. Soltani;Cong Chen

  • A Reactive Power Dispatch Strategy With Loss Minimization for a DFIG-Based Wind Farm

    Baohua Zhang;Peng Hou;Weihao Hu;Mohsen Soltani

  • Comprehensive Cost Minimization in Distribution Networks Using Segmented-Time Feeder Reconfiguration and Reactive Power Control of Distributed Generators

    Shuheng Chen;Weihao Hu;Zhe Chen

  • Flicker Mitigation by Individual Pitch Control of Variable Speed Wind Turbines With DFIG

    Yunqian Zhang;Zhe Chen;Weihao Hu;Ming Cheng

  • Data-driven optimal energy management for a wind-solar-diesel-battery-reverse osmosis hybrid energy system using a deep reinforcement learning approach

    Guozhou Zhang;Weihao Hu;Di Cao;Wen Liu

Frequent Co-Authors

Zhe Chen
Zhe Chen Aalborg University
Junbo Zhao
Junbo Zhao University of Connecticut
Birgitte Bak-Jensen
Birgitte Bak-Jensen Aalborg University
Frede Blaabjerg
Frede Blaabjerg Aalborg University
Jiakun Fang
Jiakun Fang Huazhong University of Science and Technology
Henrik Lund
Henrik Lund Aalborg University
Ming Cheng
Ming Cheng Southeast University
Mark Z. Jacobson
Mark Z. Jacobson Stanford University
Guangya Yang
Guangya Yang Technical University of Denmark
Weirong Chen
Weirong Chen Southwest Jiaotong University

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