World's Best Scientists 2026 revealed!

D-Index & Metrics

Electronics and Electrical Engineering

D-Index
64
Citations
19296
World Ranking
1272
National Ranking
528

Saifur Rahman publication distribution in Electronics and Electrical Engineering in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Electronics and Electrical Engineering in 2026. The highlighted bar marks where Saifur Rahman sits on this spectrum.

34–53 publications: 24 scientists 54–73 publications: 52 scientists 74–93 publications: 114 scientists 94–113 publications: 203 scientists 114–133 publications: 269 scientists 134–153 publications: 355 scientists 154–173 publications: 403 scientists 174–193 publications: 445 scientists 194–213 publications: 430 scientists 214–233 publications: 431 scientists 234–253 publications: 399 scientists 254–273 publications: 366 scientists 274–293 publications: 335 scientists 294–313 publications: 300 scientists 314–333 publications: 276 scientists 334–353 publications: 250 scientists 354–373 publications: 214 scientists 374–393 publications: 187 scientists 394–413 publications: 152 scientists 414–433 publications: 169 scientists 434–453 publications: 147 scientists 454–473 publications: 111 scientists 474–493 publications: 117 scientists 494–513 publications: 103 scientists 514–533 publications: 99 scientists 534–553 publications: 92 scientists 554–573 publications: 75 scientists 574–593 publications: 58 scientists 594–613 publications: 69 scientists 614–633 publications: 50 scientists 634–653 publications: 62 scientists 654–673 publications: 54 scientists 674–693 publications: 44 scientists 694–713 publications: 37 scientists 714–733 publications: 28 scientists 734–753 publications: 26 scientists 754–773 publications: 26 scientists 774–793 publications: 19 scientists 794–813 publications: 23 scientists 814–833 publications: 20 scientists 834–853 publications: 16 scientists 854–873 publications: 20 scientists 874–893 publications: 11 scientists 894–913 publications: 11 scientists 914–933 publications: 16 scientists 934–953 publications: 13 scientists 954–973 publications: 10 scientists 974–993 publications: 11 scientists 994–1,013 publications: 9 scientists 1,014–1,033 publications: 9 scientists 1,034–1,053 publications: 10 scientists 1,054–1,064 publications: 6 scientists 1,065+ publications: 99 scientists
34 publications 1,065+

This scientist: 268 publications — 50th percentile

50% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,065 publications or more.

Saifur Rahman D-index placement in Electronics and Electrical Engineering in 2026

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

30 D-Index: 178 scientists 31 D-Index: 257 scientists 32 D-Index: 263 scientists 33 D-Index: 262 scientists 34 D-Index: 244 scientists 35 D-Index: 236 scientists 36 D-Index: 211 scientists 37 D-Index: 220 scientists 38 D-Index: 214 scientists 39 D-Index: 214 scientists 40 D-Index: 205 scientists 41 D-Index: 187 scientists 42 D-Index: 194 scientists 43 D-Index: 201 scientists 44 D-Index: 155 scientists 45 D-Index: 189 scientists 46 D-Index: 148 scientists 47 D-Index: 160 scientists 48 D-Index: 134 scientists 49 D-Index: 130 scientists 50 D-Index: 141 scientists 51 D-Index: 156 scientists 52 D-Index: 108 scientists 53 D-Index: 130 scientists 54 D-Index: 112 scientists 55 D-Index: 97 scientists 56 D-Index: 111 scientists 57 D-Index: 102 scientists 58 D-Index: 108 scientists 59 D-Index: 120 scientists 60 D-Index: 103 scientists 61 D-Index: 93 scientists 62 D-Index: 92 scientists 63 D-Index: 74 scientists 64 D-Index: 77 scientists 65 D-Index: 73 scientists 66 D-Index: 64 scientists 67 D-Index: 69 scientists 68 D-Index: 60 scientists 69 D-Index: 39 scientists 70 D-Index: 57 scientists 71 D-Index: 59 scientists 72 D-Index: 46 scientists 73 D-Index: 49 scientists 74 D-Index: 38 scientists 75 D-Index: 35 scientists 76 D-Index: 32 scientists 77 D-Index: 35 scientists 78 D-Index: 31 scientists 79 D-Index: 22 scientists 80 D-Index: 34 scientists 81 D-Index: 31 scientists 82 D-Index: 34 scientists 83 D-Index: 23 scientists 84 D-Index: 18 scientists 85 D-Index: 30 scientists 86 D-Index: 19 scientists 87 D-Index: 19 scientists 88 D-Index: 20 scientists 89 D-Index: 8 scientists 90 D-Index: 17 scientists 91 D-Index: 7 scientists 92 D-Index: 14 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 12 scientists 97 D-Index: 10 scientists 98 D-Index: 10 scientists 99 D-Index: 12 scientists 100 D-Index: 16 scientists 101 D-Index: 5 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 8 scientists 105 D-Index: 9 scientists 106 D-Index: 13 scientists 107 D-Index: 4 scientists 108 D-Index: 5 scientists 109 D-Index: 10 scientists 110 D-Index: 8 scientists 111+ D-Index: 96 scientists
30 D-Index 111+

This scientist: 64 D-Index — 82nd percentile

82% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 111 D-Index or more.

Overview

Saifur Rahman is affiliated with Virginia Tech in the United States and has a strong publication record primarily in the field of engineering. Their research spans various subfields, notably electrical and electronic engineering, control and systems engineering, aerospace engineering, information systems, and building and construction.

Their work addresses multiple topics, particularly focusing on smart grid energy management and microgrid control and optimization. Other recurrent themes in their research include antenna design and analysis, energy load and power forecasting, building energy and comfort optimization, machine fault diagnosis techniques, and smart grid security and resilience.

Saifur Rahman has published extensively in several key venues, including:

  • IEEE Transactions on Magnetics
  • IEEE Antennas and Wireless Propagation Letters
  • IEEE Power and Energy Magazine
  • IEEE Transactions on Smart Grid
  • SSRN Electronic Journal

The scientist frequently collaborates with several authors, with the most frequent coauthors being Karen Hawkins, Thomas Siegert, Mary Ward-Callan, Steven Heffner, and Cecelia Jankowski.

Among the recent papers authored or coauthored by Saifur Rahman are:

  • Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks, 2020, Applied Energy
  • A Comparative Performance Analysis of ANN Algorithms for MPPT Energy Harvesting in Solar PV System, 2021, IEEE Access
  • An Edge-Cloud Integrated Solution for Buildings Demand Response Using Reinforcement Learning, 2020, IEEE Transactions on Smart Grid
  • Short-term electrical load forecasting through heuristic configuration of regularized deep neural network, 2022, Applied Soft Computing
  • An Assessment of Multistage Reward Function Design for Deep Reinforcement Learning-Based Microgrid Energy Management, 2022, IEEE Transactions on Smart Grid

The research contributions notably include developments in load forecasting frameworks, ANN algorithm performance analysis for energy harvesting, edge-cloud solutions for demand response, and reinforcement learning applications in energy management systems.

Best Publications

  • Multi-agent systems in a distributed smart grid: Design and implementation

    M. Pipattanasomporn;H. Feroze;S. Rahman

  • Analysis and evaluation of five short-term load forecasting techniques

    Ibrahim Moghram;Saifur Rahman

  • A review of recent advances in economic dispatch

    B.H. Chowdhury;S. Rahman

  • An Algorithm for Intelligent Home Energy Management and Demand Response Analysis

    M. Pipattanasomporn;M. Kuzlu;S. Rahman

  • Communication network requirements for major smart grid applications in HAN, NAN and WAN

    Murat Kuzlu;Manisa Pipattanasomporn;Saifur Rahman

  • An expert system based algorithm for short term load forecast

    S. Rahman;R. Bhatnagar

  • Unit sizing and control of hybrid wind-solar power systems

    R. Chedid;S. Rahman

  • Grid Integration of Electric Vehicles and Demand Response With Customer Choice

    Shengnan Shao;M. Pipattanasomporn;S. Rahman

  • Challenges of PHEV penetration to the residential distribution network

    Shengnan Shao;Manisa Pipattanasomporn;Saifur Rahman

  • A decision support technique for the design of hybrid solar-wind power systems

    R. Chedid;H. Akiki;S. Rahman

  • Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques

    Mengmeng Cai;Manisa Pipattanasomporn;Saifur Rahman

  • Demand Response as a Load Shaping Tool in an Intelligent Grid With Electric Vehicles

    Shengnan Shao;M. Pipattanasomporn;S. Rahman

  • Load Profiles of Selected Major Household Appliances and Their Demand Response Opportunities

    Manisa Pipattanasomporn;Murat Kuzlu;Saifur Rahman;Yonael Teklu

  • A generalized knowledge-based short-term load-forecasting technique

    S. Rahman;O. Hazim

  • An investigation into the impact of electric vehicle load on the electric utility distribution system

    S. Rahman;G.B. Shrestha

  • Development of physical-based demand response-enabled residential load models

    Shengnan Shao;Manisa Pipattanasomporn;Saifur Rahman

  • Impact of TOU rates on distribution load shapes in a smart grid with PHEV penetration

    Shengnan Shao;Tianshu Zhang;Manisa Pipattanasomporn;Saifur Rahman

  • Two-loop controller for maximizing performance of a grid-connected photovoltaic-fuel cell hybrid power plant

    Kyoungsoo Ro;S. Rahman

  • Distribution Voltage Regulation Through Active Power Curtailment With PV Inverters and Solar Generation Forecasts

    Shibani Ghosh;Saifur Rahman;Manisa Pipattanasomporn

  • Patterned langmuir-blodgett films of monodisperse nanoparticles of iron oxide using soft lithography.

    Qijie Guo;Xiaowei Teng;Saifur Rahman;Hong Yang

  • An adaptive linear combiner for on-line tracking of power system harmonics

    P.K. Dash;D.P. Swain;A.C. Liew;S. Rahman

  • Input variable selection for ANN-based short-term load forecasting

    I. Drezga;S. Rahman

Frequent Co-Authors

Manisa Pipattanasomporn
Manisa Pipattanasomporn Chulalongkorn University
P.K. Dash
P.K. Dash Siksha O Anusandhan University
Badrul H. Chowdhury
Badrul H. Chowdhury University of North Carolina at Charlotte
A.C. Liew
A.C. Liew National University of Singapore
Haiwang Zhong
Haiwang Zhong Tsinghua University
Qing Xia
Qing Xia Tsinghua University
Yonghua Song
Yonghua Song University of Macau
Chongqing Kang
Chongqing Kang Tsinghua University
Yi Ding
Yi Ding Zhejiang University
Fred C. Lee
Fred C. Lee Virginia Tech

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