Katarina Grolinger

5.1k total citations · 1 hit paper
72 papers, 3.3k citations indexed

About

Katarina Grolinger is a scholar working on Artificial Intelligence, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Katarina Grolinger has authored 72 papers receiving a total of 3.3k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Artificial Intelligence, 23 papers in Electrical and Electronic Engineering and 15 papers in Computer Vision and Pattern Recognition. Recurrent topics in Katarina Grolinger's work include Energy Load and Power Forecasting (18 papers), Smart Grid Energy Management (9 papers) and Building Energy and Comfort Optimization (8 papers). Katarina Grolinger is often cited by papers focused on Energy Load and Power Forecasting (18 papers), Smart Grid Energy Management (9 papers) and Building Energy and Comfort Optimization (8 papers). Katarina Grolinger collaborates with scholars based in Canada, Egypt and France. Katarina Grolinger's co-authors include Miriam A. M. Capretz, Hany F. ElYamany, Mohammad Navid Fekri, Mauro C. C. Ribeiro, Wilson A. Higashino, Syed Mir, Vinay Sharma, Girma Bitsuamlak, Miriam A. M. Capretz and Abhinav Tiwari and has published in prestigious journals such as Scientific Reports, Applied Energy and IEEE Access.

In The Last Decade

Katarina Grolinger

63 papers receiving 3.1k citations

Hit Papers

Machine Learning With Big Data: Challenges and Approaches 2017 2026 2020 2023 2017 200 400 600
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Machine Learning With Big Data: Challenges and Approaches
    2017 660 citations Katarina Grolinger, Hany F. ElYamany et al. IEEE Access profile →

Peers

Katarina Grolinger
Yassine Himeur United Arab Emirates
Leye Wang China
Miriam A. M. Capretz Canada
Sung-Bae Cho South Korea
Fayçal Bensaali Qatar
Musaed Alhussein Saudi Arabia
Yongjun Xu China
Wolfgang Kästner Austria
Milos Manic United States
Weiwei Jiang China
Yassine Himeur United Arab Emirates
Katarina Grolinger
Citations per year, relative to Katarina Grolinger Katarina Grolinger (= 1×) peers Yassine Himeur

Countries citing papers authored by Katarina Grolinger

Since Specialization
Citations

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

Fields of papers citing papers by Katarina Grolinger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Katarina Grolinger

This figure shows the co-authorship network connecting the top 25 collaborators of Katarina Grolinger. A scholar is included among the top collaborators of Katarina Grolinger based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Katarina Grolinger. Katarina Grolinger is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Ali, Kashif, et al.. (2025). Physics-Guided Memory Network for building energy modeling. Energy and AI. 21. 100538–100538.
2.
Fekri, Mohammad Navid, et al.. (2025). Federated Online Learning for adaptive load forecasting across decentralized nodes. International Journal of Electrical Power & Energy Systems. 169. 110779–110779.
3.
Bitsuamlak, Girma, et al.. (2025). Data-driven and physics-based modeling approaches and their integration in building digital twins: A systematic review. Journal of Building Engineering. 114. 114214–114214.
4.
Dunphy, Kyle, et al.. (2025). Few-Shot Learning Augmented with Image Transformation for Multiclass Structural Damage Classification. Journal of Computing in Civil Engineering. 39(3). 2 indexed citations
5.
Grolinger, Katarina, et al.. (2024). Kolmogorov–Arnold recurrent network for short term load forecasting across diverse consumers. Energy Reports. 13. 713–727. 9 indexed citations
6.
7.
Grolinger, Katarina, et al.. (2024). Leveraging Hypernetworks and Learnable Kernels for Consumer Energy Forecasting Across Diverse Consumer Types. IEEE Transactions on Power Delivery. 40(1). 75–87. 3 indexed citations
8.
Grolinger, Katarina, et al.. (2024). Performance Analysis of Dilated One-to-Many U-Net Model for Medical Image Segmentation. IEEE Access. 12. 197259–197274. 2 indexed citations
9.
Grolinger, Katarina, et al.. (2024). Graph Attention Convolutional U-NET: A Semantic Segmentation Model for Identifying Flooded Areas. 1–6. 1 indexed citations
10.
Grolinger, Katarina, et al.. (2023). Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging. Energies. 16(10). 4093–4093. 8 indexed citations
11.
Grolinger, Katarina, et al.. (2022). Transformer-Based Model for Electrical Load Forecasting. Energies. 15(14). 4993–4993. 83 indexed citations
12.
Fekri, Mohammad Navid, et al.. (2019). Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks. Energies. 13(1). 130–130. 101 indexed citations
13.
Ribeiro, Mauro C. C., Katarina Grolinger, Hany F. ElYamany, Wilson A. Higashino, & Miriam A. M. Capretz. (2018). Transfer learning with seasonal and trend adjustment for cross-building energy forecasting. Energy and Buildings. 165. 352–363. 163 indexed citations
14.
Ribeiro, Mauro C. C., Katarina Grolinger, & Miriam A. M. Capretz. (2015). MLaaS: Machine Learning as a Service. 896–902. 239 indexed citations
15.
Grolinger, Katarina, et al.. (2013). EEF-CAS: An Effort Estimation Framework with Customizable Attribute Selection. International Journal of Advancements in Computing Technology. 5(13). 14–33. 3 indexed citations
16.
Grolinger, Katarina. (2013). Disaster Data Management in Cloud Environments. Scholarship@Western (Western University).
17.
Grolinger, Katarina & Miriam A. M. Capretz. (2012). Autonomic Database Management: State of the Art and Future Trends. Scholarship@Western (Western University). 276–281. 1 indexed citations
18.
Grolinger, Katarina, Miriam A. M. Capretz, José R. Martí, & K.D. Srivastava. (2012). Ontology–based Representation of Simulation Models. Scholarship@Western (Western University). 432–437. 5 indexed citations
19.
Grolinger, Katarina, et al.. (2011). From Glossaries to Ontologies: Disaster Management Domain. Scholarship@Western (Western University). 402–407. 5 indexed citations
20.
Grolinger, Katarina. (2011). Problem Based Learning in Engineering Education: Meeting the needs of industry. Scholarship@Western (Western University). 1(2). 2. 5 indexed citations

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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