Keda Pan

462 citations
13 papers · 361 · h-index 9

Impact in

Papers in

Keda Pan

12 papers receiving 354 citations

Peers

Keda Pan
Comparison fields: 5 of 47
  • Energy Engineering and Power Technology 20
  • Artificial Intelligence 166
  • Electrical and Electronic Engineering 292
  • Management Science and Operations Research 51
  • Renewable Energy, Sustainability and the Environment 53
Replace Mucun Sun with:
Mucun Sun United States
Danxiang Wei Macao
Adil Ahmed Saudi Arabia
Meftah Elsaraiti Canada
Shuwei Miao China
H. J. Lu Taiwan
Mingheng Chang China
M. Ghayekhloo United States
Luca Massidda Italy
Emrah Dokur Türkiye
Keda Pan relative to Mucun Sun United States Mucun Sun's profile →
Citations per field
00.5×1.5×
Mucun Sun · 1×
Citations per year

Countries citing papers authored by Keda Pan

Since Specialization
Citations

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

Fields of papers citing papers by Keda Pan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

13 of 13 papers shown
#Work
1 2018142
2 202164
3 202044
4 202124
5 202123
6 202218
7 202117
8 202012
9 20229
10 20184
11 20183
12 20211
13 20230

About Keda Pan

Keda Pan is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence, Renewable Energy, Sustainability and the Environment, Energy Engineering and Power Technology and Management Science and Operations Research, having authored 13 papers that have together received 361 indexed citations. Recurring topics across this work include Energy Load and Power Forecasting (9 papers), Solar Radiation and Photovoltaics (8 papers), Smart Grid Energy Management (4 papers), Power Systems and Renewable Energy (3 papers), Photovoltaic System Optimization Techniques (3 papers), Electric Power System Optimization (2 papers), Grey System Theory Applications (2 papers) and Machine Learning and ELM (2 papers). The work is most often cited by research in Energy Engineering and Power Technology (20 citations), Artificial Intelligence (166 citations), Electrical and Electronic Engineering (292 citations), Management Science and Operations Research (51 citations) and Renewable Energy, Sustainability and the Environment (53 citations). Keda Pan has collaborated with scholars based in China, United Kingdom and Australia. Frequent co-authors include Dan Zhang, Xiangang Peng, Yi Liu, Chun Sing Lai, Loi Lei Lai, Wing W. Y. Ng, Ting Wang, Zhuoli Zhao, Dongxiao Wang and Mohammad Shahidehpour. Their work appears in journals such as International Journal of Machine Learning and Cybernetics, Sustainable Cities and Society, IEEE Transactions on Smart Grid, Applied Energy and Energy Conversion and Management.

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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