Lingjun Zhang

66 papers receiving 2.4k citations

Lingjun Zhang's Hit Papers

A review of wind speed and wind power forecasting with deep neural networks 2021 · 682 citations
6820+1+3Years since publication200400600

Peers

Lingjun Zhang
Comparison fields: 5 of 119
  • Process Chemistry and Technology 78
  • Energy Engineering and Power Technology 80
  • Electrical and Electronic Engineering 1.2k
  • Electronic, Optical and Magnetic Materials 291
  • Polymers and Plastics 197
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Yonghui Sun China
Junjie Tang China
Chunming Liu China
Shuying Cheng China
Huifeng Zhang China
Sai Li China
Zichen Zhang China
Yang Su China
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Citations per year

Countries citing papers authored by Lingjun Zhang

Since Specialization
Citations

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

Fields of papers citing papers by Lingjun Zhang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Lingjun Zhang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Lingjun Zhang Line = papers co-authored together Lingjun Zhang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 68 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A review of wind speed and wind power forecasting with deep neural networks
Hit paper breakdown →
2021682
2 2013155
3 2017137
4 2014110
5 201793
6 200186
7 202382
8 202169
9 201267
10 201460
11 201447
12 200346
13 202241
14 201840
15 201739
16 202235
17 201535
18 202134
19 202130
20 202228

About Lingjun Zhang

Lingjun Zhang is a scholar working on Electronic, Optical and Magnetic Materials, Electrical and Electronic Engineering, Polymers and Plastics, Biomedical Engineering and Computational Theory and Mathematics, having authored 68 papers that have together received 2.4k indexed citations. Recurring topics across this work include Gold and Silver Nanoparticles Synthesis and Applications (8 papers), Rough Sets and Fuzzy Logic (7 papers), Advanced Sensor and Energy Harvesting Materials (6 papers), Silicon and Solar Cell Technologies (5 papers), Text and Document Classification Technologies (4 papers), Electric Power System Optimization (4 papers), Mineral Processing and Grinding (4 papers) and Organic Electronics and Photovoltaics (4 papers). The work is most often cited by research in Process Chemistry and Technology (78 citations), Energy Engineering and Power Technology (80 citations), Electrical and Electronic Engineering (1.2k citations), Electronic, Optical and Magnetic Materials (291 citations) and Polymers and Plastics (197 citations). Lingjun Zhang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Yun Wang, Runmin Zou, Fang Liu, Qianyi Liu, Qinghua Hu, Yingzhou Huang, Xiaodong Su, Yucan Zhou, Witold Pedrycz and Mingrong Shen. Their work appears in journals such as The Science of The Total Environment, Renewable Energy, Nano Energy, IEEE Journal of Photovoltaics and Industrial & Engineering Chemistry Research.

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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