Jun Zhao

6.6k citations
363 papers · 4.4k indexed · 1 hit paper · h-index 32

Impact in

Papers in

Jun Zhao

326 papers receiving 4.3k citations

Hit Papers

A Review of Deep Learning Models for Time Series Prediction 2019 · 314 citations
3142019202620212023100200300

Peers

Jun Zhao
Comparison fields: 5 of 176
  • Control and Systems Engineering 1.1k
  • Energy Engineering and Power Technology 128
  • Industrial and Manufacturing Engineering 359
  • Bioengineering 176
  • Artificial Intelligence 969
Replace Qiang Yang with:
Qiang Yang China
Kincho H. Law United States
Chen Lü China
Jie Liu China
Ví­ctor M. Zavala United States
Thilo Sauter Austria
Thomas F. Edgar United States
Kang Li United Kingdom
Guanjun Liu China
Weihua Gui China
Jun Zhao relative to Qiang Yang China Qiang Yang's profile →
Citations per field
00.5×4.8×
Qiang Yang · 1×
Citations per year

Countries citing papers authored by Jun Zhao

Since Specialization
Citations

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

Fields of papers citing papers by Jun Zhao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Jun Zhao, 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 Jun Zhao Line = papers co-authored together Jun Zhao links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
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20 201718

About Jun Zhao

Jun Zhao is a scholar working on Control and Systems Engineering, Artificial Intelligence, Industrial and Manufacturing Engineering, Energy Engineering and Power Technology and Fuel Technology, having authored 363 papers that have together received 4.4k indexed citations. Recurring topics across this work include Fault Detection and Control Systems (41 papers), Neural Networks and Applications (33 papers), Topic Modeling (22 papers), Natural Language Processing Techniques (21 papers), Integrated Energy Systems Optimization (18 papers), Energy Load and Power Forecasting (17 papers), Smart Grid Energy Management (14 papers) and Microgrid Control and Optimization (14 papers). The work is most often cited by research in Control and Systems Engineering (1.1k citations), Energy Engineering and Power Technology (128 citations), Industrial and Manufacturing Engineering (359 citations), Bioengineering (176 citations) and Artificial Intelligence (969 citations). Jun Zhao has collaborated with scholars based in China, Canada and United Kingdom. Frequent co-authors include Wei Wang, Zhongyang Han, Henry Leung, Witold Pedrycz, Quanli Liu, Shuai Liu, David J. Hill, Qiyuan Peng, King Ma and Wei Wang. Their work appears in journals such as Information Sciences, Control Engineering Practice, IEEE Transactions on Industrial Electronics, IEEE Transactions on Instrumentation and Measurement and Applied Thermal Engineering.

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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