Lingjun Zhang

63 papers receiving 2.1k citations

Lingjun Zhang's Hit Papers

A review of wind speed and wind power forecasting with deep neural networks 2021 · 589 citations
5890+1+3Years since publication100200300400500

Peers

Lingjun Zhang
Comparison fields: 5 of 123
  • Process Chemistry and Technology 75
  • Energy Engineering and Power Technology 78
  • Electrical and Electronic Engineering 1.1k
  • Electronic, Optical and Magnetic Materials 273
  • Polymers and Plastics 194
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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 66 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 →
2021589
2 2013145
3 2017129
4 2014106
5 201785
6 200181
7 202375
8 202166
9 201459
10 201257
11 201447
12 200341
13 201739
14 202238
15 201836
16 202233
17 201533
18 202129
19 202227
20 201926

About Lingjun Zhang

Lingjun Zhang is a scholar working on Electrical and Electronic Engineering, Biomedical Engineering, Electronic, Optical and Magnetic Materials, Mechanical Engineering and Materials Chemistry, having authored 66 papers that have together received 2.2k indexed citations. Recurring topics across this work include Gold and Silver Nanoparticles Synthesis and Applications (8 papers), Advanced Sensor and Energy Harvesting Materials (6 papers), Rough Sets and Fuzzy Logic (5 papers), Conducting polymers and applications (4 papers), Fault Detection and Control Systems (4 papers), Biosensors and Analytical Detection (4 papers), Organometallic Complex Synthesis and Catalysis (4 papers) and Energy Load and Power Forecasting (4 papers). The work is most often cited by research in Process Chemistry and Technology (75 citations), Energy Engineering and Power Technology (78 citations), Electrical and Electronic Engineering (1.1k citations), Electronic, Optical and Magnetic Materials (273 citations) and Polymers and Plastics (194 citations). Lingjun Zhang has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Yun Wang, Runmin Zou, Qianyi Liu, Fang Liu, Xiaodong Su, Qinghua Hu, Yingzhou Huang, Yucan Zhou, Witold Pedrycz and Mingrong Shen. Their work appears in journals such as The Science of The Total Environment, Nano Energy, Applied Energy, Industrial & Engineering Chemistry Research and Renewable Energy.

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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