Can Wan

5.8k citations
111 papers · 4.4k · 1 hit paper · h-index 39

Impact in

Papers in

Can Wan

100 papers receiving 4.3k citations

Can Wan's Hit Papers

Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine 2013 · 606 citations
6060+4+8Years since publication200400600

Peers

Can Wan
Comparison fields: 5 of 97
  • Energy Engineering and Power Technology 363
  • Electrical and Electronic Engineering 3.8k
  • Control and Systems Engineering 1.2k
  • Safety, Risk, Reliability and Quality 257
  • Artificial Intelligence 858
Replace Qixin Chen with:
Qixin Chen China
Peter Pálenský Netherlands
Eleonora Riva Sanseverino Italy
Long Zhao United States
Tao Zhang China
Qing‐Shan Jia China
Yue Zhou China
Ettore Bompard Italy
Cheng Wang China
Farshid Keynia Iran
Can Wan relative to Qixin Chen China Qixin Chen's profile →
Citations per field
00.5×1.5×
Qixin Chen · 1×
Citations per year

Countries citing papers authored by Can Wan

Since Specialization
Citations

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

Fields of papers citing papers by Can Wan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 111 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine
Hit paper breakdown →
2013606
2 2014276
3 2016233
4 2013186
5 2016140
6 2018131
7 2018121
8 2013113
9 2015103
10 201799
11 201996
12 201395
13 201693
14 201293
15 201486
16 201884
17 201978
18 201673
19 201872
20 202170

About Can Wan

Can Wan is a scholar working on Electrical and Electronic Engineering, Control and Systems Engineering, Artificial Intelligence, Safety, Risk, Reliability and Quality and Management Science and Operations Research, having authored 111 papers that have together received 4.4k indexed citations. Recurring topics across this work include Energy Load and Power Forecasting (40 papers), Smart Grid Energy Management (38 papers), Electric Power System Optimization (34 papers), Optimal Power Flow Distribution (33 papers), Microgrid Control and Optimization (24 papers), Machine Learning and ELM (13 papers), Power System Reliability and Maintenance (11 papers) and Integrated Energy Systems Optimization (11 papers). The work is most often cited by research in Energy Engineering and Power Technology (363 citations), Electrical and Electronic Engineering (3.8k citations), Control and Systems Engineering (1.2k citations), Safety, Risk, Reliability and Quality (257 citations) and Artificial Intelligence (858 citations). Can Wan has collaborated with scholars based in China, Macao and Hong Kong. Frequent co-authors include Yonghua Song, Zhao Xu, Zhao Yang Dong, Kit Po Wong, Pierre Pinson, Jin Lin, Jianhui Wang, Changfei Zhao, Erbao Cao and Yibao Jiang. Their work appears in journals such as IEEE Transactions on Power Systems, IEEE Transactions on Smart Grid, IEEE Transactions on Sustainable Energy, Journal of Modern Power Systems and Clean Energy and IET Generation Transmission & Distribution.

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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