Nils Raabe

63 papers receiving 1.3k citations

Nils Raabe's Hit Papers

Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks 2016 · 425 citations
4250+3+6Years since publication100200300400

Peers

Nils Raabe
Comparison fields: 5 of 132
  • Anesthesiology and Pain Medicine 52
  • Surgery 352
  • Renewable Energy, Sustainability and the Environment 126
  • Artificial Intelligence 242
  • Developmental Neuroscience 28
Replace Mark Carpenter with:
Mark Carpenter United States
Jian‐Guo Zhou China
Jun Dai China
Jianrong Zhang China
Lorenzo Bianchi Italy
Zhifei Sun United States
Patrick Cheung Canada
Masato Nakayama Japan
Weiwei Wang China
Morihito Okada Japan
Nils Raabe relative to Mark Carpenter United States Mark Carpenter's profile →
Citations per field
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Citations per year

Countries citing papers authored by Nils Raabe

Since Specialization
Citations

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

Fields of papers citing papers by Nils Raabe

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Nils Raabe, 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 Nils Raabe Line = papers co-authored together Nils Raabe 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
Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks
Hit paper breakdown →
2016425
2 200585
3 200669
4 197461
5 199760
6 198157
7 198652
8 200851
9 197534
10 199732
11 197529
12 198926
13 197623
14 199222
15 197621
16 201719
17 199518
18 201718
19 199117
20 199616

About Nils Raabe

Nils Raabe is a scholar working on Surgery, Cancer Research, Pulmonary and Respiratory Medicine, Atomic and Molecular Physics, and Optics and Oncology, having authored 66 papers that have together received 1.4k indexed citations. Recurring topics across this work include Anesthesia and Pain Management (12 papers), Breast Cancer Treatment Studies (11 papers), Laser-Matter Interactions and Applications (9 papers), Advanced Fiber Laser Technologies (7 papers), Cardiac, Anesthesia and Surgical Outcomes (6 papers), Prostate Cancer Treatment and Research (5 papers), Estrogen and related hormone effects (5 papers) and Anesthesia and Sedative Agents (4 papers). The work is most often cited by research in Anesthesiology and Pain Medicine (52 citations), Surgery (352 citations), Renewable Energy, Sustainability and the Environment (126 citations), Artificial Intelligence (242 citations) and Developmental Neuroscience (28 citations). Nils Raabe has collaborated with scholars based in Norway, Sweden and Germany. Frequent co-authors include Bernhard Sick, Janosch Henze, André Gensler, Patrick Belfrage, B Thalme, Sophie D. Fosså, Michael Spiteller, Andrija Šmelcerović, L. Irestedt and Anita Berlin. Their work appears in journals such as Acta Obstetricia Et Gynecologica Scandinavica, Acta Oncologica, European Journal of Cancer, Optics Letters and American Journal of Obstetrics and Gynecology.

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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