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Jianzhou Wang
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Engineering and Technology
China
2026

D-Index & Metrics

Discipline name D-Index World Ranking Current World Ranking National Ranking Current National Ranking Publications Citations
Engineering and Technology 90 277 264 48 47 297 22461

Jianzhou Wang publications per year

The chart shows the history of publications by Jianzhou Wang between 1994 and 2025, highlighting the no. of papers published in each year and offering an overview of the publication velocity of this scholar. Jianzhou Wang published across 32 years, from 1994 to 2025, averaging 11.8 papers a year. Output peaked at 45 publications in 2022. 59 of the 378 publications appeared in the last two years.

No. of publications
10 20 30 40
Bar chart. Horizontal axis: year, 1994 to 2025. Vertical axis: number of publications, 0 to 45. Peak 45 publications in 2022. 1994: 1 publication 1995: 0 publications 1996: 0 publications 1997: 0 publications 1998: 1 publication 1999: 1 publication 2000: 0 publications 2001: 0 publications 2002: 0 publications 2003: 0 publications 2004: 0 publications 2005: 1 publication 2006: 0 publications 2007: 0 publications 2008: 15 publications 2009: 5 publications 2010: 4 publications 2011: 9 publications 2012: 11 publications 2013: 7 publications 2014: 19 publications 2015: 19 publications 2016: 20 publications 2017: 23 publications 2018: 18 publications 2019: 22 publications 2020: 20 publications 2021: 40 publications 2022: 45 publications 2023: 38 publications 2024: 39 publications 2025: 20 publications
1994 2025

378 publications in total across all disciplines

View publications per year as a table
Jianzhou Wang: publications per year, 1994 to 2025
Year Publications
1994 1
1995 0
1996 0
1997 0
1998 1
1999 1
2000 0
2001 0
2002 0
2003 0
2004 0
2005 1
2006 0
2007 0
2008 15
2009 5
2010 4
2011 9
2012 11
2013 7
2014 19
2015 19
2016 20
2017 23
2018 18
2019 22
2020 20
2021 40
2022 45
2023 38
2024 39
2025 20
Total 378
Download as CSV

Jianzhou Wang 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 Jianzhou Wang sits on this spectrum.

No. of scientists
100 200 300 400
Bar chart with 78 bars. Horizontal axis: publications, 38–47 to 804+. Vertical axis: number of scientists, 0 to 457. Most scientists, 457, have 148–157 publications. The last bar groups every scientist with 804 publications or more. The highlighted bar, 288–297 publications, is where this scientist sits. 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
38–47 publications 804+

This scientist: 297 publications — 75th percentile

75% of scientists in this discipline score the same or lower.

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

View publications distribution as a table
Number of Engineering and Technology scientists by publication count, Research.com 2026 ranking edition. Based on 9,796 ranked scientists.
Publications Scientists This scientist
38–47 20
48–57 35
58–67 96
68–77 135
78–87 190
88–97 259
98–107 283
108–117 369
118–127 341
128–137 386
138–147 372
148–157 457
158–167 415
168–177 407
178–187 421
188–197 378
198–207 403
208–217 317
218–227 346
228–237 321
238–247 260
248–257 280
258–267 240
268–277 214
278–287 242
288–297 203 297
298–307 166
308–317 154
318–327 175
328–337 159
338–347 99
348–357 131
358–367 106
368–377 118
378–387 97
388–397 108
398–407 82
408–417 71
418–427 64
428–437 55
438–447 54
448–457 60
458–467 47
468–477 40
478–487 30
488–497 29
498–507 38
508–517 40
518–527 32
528–537 23
538–547 28
548–557 23
558–567 19
568–577 16
578–587 17
588–597 18
598–607 22
608–617 15
618–627 9
628–637 11
638–647 21
648–657 12
658–667 9
668–677 11
678–687 9
688–697 6
698–707 14
708–717 7
718–727 8
728–737 10
738–747 9
748–757 5
758–767 5
768–777 11
778–787 7
788–797 2
798–803 4
804+ 100
Download as CSV

Jianzhou Wang 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 Jianzhou Wang sits on this spectrum.

No. of scientists
100 200 300 400
Bar chart with 78 bars. Horizontal axis: D-Index, 30 to 107+. Vertical axis: number of scientists, 0 to 426. Most scientists, 426, have 42 D-Index. The last bar groups every scientist with 107 D-Index or more. The highlighted bar, 90 D-Index, is where this scientist sits. 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: 90 D-Index — 97th percentile

97% of scientists in this discipline score the same or lower.

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

View D-Index distribution as a table
Number of Engineering and Technology scientists by D-index, Research.com 2026 ranking edition. Based on 9,796 ranked scientists.
D-Index Scientists This scientist
30 59
31 114
32 129
33 189
34 200
35 262
36 311
37 312
38 350
39 385
40 348
41 362
42 426
43 380
44 310
45 341
46 301
47 306
48 271
49 246
50 210
51 253
52 213
53 221
54 195
55 186
56 170
57 167
58 166
59 144
60 152
61 141
62 138
63 131
64 118
65 114
66 119
67 95
68 87
69 77
70 89
71 69
72 54
73 46
74 55
75 54
76 49
77 53
78 46
79 28
80 39
81 36
82 24
83 26
84 36
85 18
86 25
87 19
88 26
89 27
90 23 90
91 15
92 12
93 9
94 15
95 10
96 13
97 13
98 9
99 7
100 7
101 8
102 7
103 7
104 9
105 6
106 9
107+ 99
Download as CSV

Research.com Recognitions

  • 2026 - Research.com Engineering and Technology in China Leader Award
  • 2025 - Research.com Engineering and Technology in China Leader Award

Overview

Jianzhou Wang is a researcher affiliated with Macau University of Science and Technology in China. Their scholarly work primarily falls within the field of Engineering, with a significant focus on subfields such as Electrical and Electronic Engineering, Management Science and Operations Research, Artificial Intelligence, Environmental Engineering, and Economics and Econometrics.

The main research topics explored by Jianzhou Wang include:

  • Energy Load and Power Forecasting
  • Grey System Theory Applications
  • Electric Power System Optimization
  • Solar Radiation and Photovoltaics
  • Stock Market Forecasting Methods
  • Air Quality Monitoring and Forecasting
  • Wind Energy Research and Development

Jianzhou Wang has published extensively across various academic venues, with frequent contributions to:

  • Expert Systems with Applications
  • Applied Soft Computing
  • Applied Energy
  • Engineering Applications of Artificial Intelligence
  • SSRN Electronic Journal

Some of the recent papers authored or co-authored by Jianzhou Wang include:

  • Ensemble forecasting system for short-term wind speed forecasting based on optimal sub-model selection and multi-objective version of mayfly optimization algorithm (2021), Expert Systems with Applications
  • A novel hybrid model based on multi-objective Harris hawks optimization algorithm for daily PM2.5 and PM10 forecasting (2020), Applied Soft Computing
  • Developing a deep learning framework with two-stage feature selection for multivariate financial time series forecasting (2020), Expert Systems with Applications
  • Forecasting Chinese carbon emissions using a novel grey rolling prediction model (2021), Chaos Solitons & Fractals
  • Impacts of haze pollution on China's tourism industry: A system of economic loss analysis (2021), Journal of Environmental Management

Collaborations form a notable part of Jianzhou Wang's research activity. Frequent co-authors include:

  • Zhenkun Liu
  • Zhiwu Li
  • Lifang Zhang
  • Xinsong Niu
  • Mengzheng Lv

The diverse topics and publication venues indicate a multidisciplinary approach with a strong foundation in engineering methodologies, optimization algorithms, and environmental and financial forecasting systems. This body of work reflects ongoing engagement with contemporary challenges related to power systems, air quality, and economic modeling.

Best Publications

  • Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model

    Zhenhai Guo;Weigang Zhao;Haiyan Lu;Jianzhou Wang

  • Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting

    Chao Ren;Ning An;Jianzhou Wang;Lian Li

  • Stock index forecasting based on a hybrid model

    Ju-Jie Wang;Jian-Zhou Wang;Zhe-George Zhang;Zhe-George Zhang;Shu-Po Guo

  • Air Pollution Forecasts: An Overview

    Lu Bai;Jianzhou Wang;Xuejiao Ma;Haiyan Lu

  • A case study on a hybrid wind speed forecasting method using BP neural network

    Zhen-hai Guo;Jie Wu;Hai-yan Lu;Jian-zhou Wang

  • A robust combination approach for short-term wind speed forecasting and analysis – Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model

    Jianzhou Wang;Jianming Hu

  • Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm

    Xiaobo Zhang;Jianzhou Wang;Kequan Zhang

  • A novel hybrid system based on a new proposed algorithm-Multi-Objective Whale Optimization Algorithm for wind speed forecasting

    Jianzhou Wang;Pei Du;Tong Niu;Wendong Yang

  • Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression

    Jianming Hu;Jianzhou Wang

  • A novel hybrid model for short-term wind power forecasting

    Pei Du;Jianzhou Wang;Wendong Yang;Tong Niu

  • Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models

    Jianzhou Wang;Yiliao Song;Yiliao Song;Feng Liu;Feng Liu;Ru Hou

  • A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting

    Jingjing Song;Jianzhou Wang;Haiyan Lu

  • Wind speed probability distribution estimation and wind energy assessment

    Jianzhou Wang;Jianming Hu;Kailiang Ma

  • A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm

    Jianzhou Wang;Wendong Yang;Pei Du;Tong Niu

  • The Study and Application of a Novel Hybrid Forecasting Model — A Case Study of Wind Speed Forecasting in China

    Jian-Zhou Wang;Yun Wang;Ping Jiang

  • Can China realize its carbon emission reduction goal in 2020: From the perspective of thermal power development

    Unknown

  • A novel hybrid system based on multi-objective optimization for wind speed forecasting

    Chunying Wu;Jianzhou Wang;Xuejun Chen;Pei Du

  • Combined forecasting models for wind energy forecasting: A case study in China

    Ling Xiao;Jianzhou Wang;Yao Dong;Jie Wu

  • Air quality early-warning system for cities in China

    Yunzhen Xu;Wendong Yang;Jianzhou Wang

  • Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system

    Xuejing Zhao;Chen Wang;Jinxia Su;Jianzhou Wang

  • An improved grey model optimized by multi-objective ant lion optimization algorithm for annual electricity consumption forecasting

    Jianzhou Wang;Pei Du;Haiyan Lu;Wendong Yang

  • Research and application of a novel hybrid forecasting system based on multi-objective optimization for wind speed forecasting

    Pei Du;Jianzhou Wang;Jianzhou Wang;Zhenhai Guo;Wendong Yang

  • A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting

    Wendong Yang;Jianzhou Wang;Tong Niu;Pei Du

Frequent Co-Authors

Haiyan Lu
Haiyan Lu University of Technology Sydney
Zhiwu Li
Zhiwu Li Macau University of Science and Technology

External Links

Google Scholar page

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