Samuel Prívara

2.1k total citations · 2 hit papers
21 papers, 1.7k citations indexed

About

Samuel Prívara is a scholar working on Control and Systems Engineering, Building and Construction and Civil and Structural Engineering. According to data from OpenAlex, Samuel Prívara has authored 21 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Control and Systems Engineering, 15 papers in Building and Construction and 4 papers in Civil and Structural Engineering. Recurrent topics in Samuel Prívara's work include Advanced Control Systems Optimization (16 papers), Building Energy and Comfort Optimization (14 papers) and Control Systems and Identification (13 papers). Samuel Prívara is often cited by papers focused on Advanced Control Systems Optimization (16 papers), Building Energy and Comfort Optimization (14 papers) and Control Systems and Identification (13 papers). Samuel Prívara collaborates with scholars based in Czechia and Switzerland. Samuel Prívara's co-authors include Jiří Cigler, Ján Široký, Frauke Oldewurtel, Lukáš Ferkl, Zdeněk Váňa, Eva Žáčeková, Carina Sagerschnig, Dimitrios Gyalistras, Manfred Morari and Michael Šebek and has published in prestigious journals such as Applied Energy, Energy and Buildings and Tunnelling and Underground Space Technology.

In The Last Decade

Samuel Prívara

21 papers receiving 1.6k citations

Hit Papers

Experimental analysis of model predictive control for an ... 2010 2026 2015 2020 2011 2010 200 400 600

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Samuel Prívara Czechia 12 1.5k 595 496 364 335 21 1.7k
Jiří Cigler Czechia 14 1.6k 1.1× 653 1.1× 550 1.1× 404 1.1× 368 1.1× 29 1.9k
Markus Gwerder Switzerland 13 1.5k 1.0× 632 1.1× 637 1.3× 426 1.2× 419 1.3× 20 1.9k
Ján Široký Czechia 9 1.0k 0.7× 450 0.8× 343 0.7× 276 0.8× 238 0.7× 21 1.2k
Donghun Kim United States 19 1.1k 0.7× 462 0.8× 522 1.1× 240 0.7× 268 0.8× 75 1.5k
B. Lehmann Switzerland 11 1.5k 1.0× 378 0.6× 433 0.9× 490 1.3× 510 1.5× 15 1.9k
Christian Ghiaus France 20 1.1k 0.8× 167 0.3× 235 0.5× 562 1.5× 188 0.6× 54 1.4k
Ján Drgoňa United States 14 897 0.6× 574 1.0× 482 1.0× 188 0.5× 263 0.8× 46 1.4k
Brandon Hencey United States 16 732 0.5× 589 1.0× 404 0.8× 140 0.4× 155 0.5× 45 1.2k
Lukáš Ferkl Czechia 12 653 0.4× 337 0.6× 199 0.4× 164 0.5× 129 0.4× 31 881
David Blum United States 14 928 0.6× 378 0.6× 566 1.1× 193 0.5× 269 0.8× 29 1.3k

Countries citing papers authored by Samuel Prívara

Since Specialization
Citations

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

Fields of papers citing papers by Samuel Prívara

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Samuel Prívara

This figure shows the co-authorship network connecting the top 25 collaborators of Samuel Prívara. A scholar is included among the top collaborators of Samuel Prívara 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 Samuel Prívara. Samuel Prívara 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.
Prívara, Samuel, et al.. (2013). Dual control approach for zone model predictive control. 1398–1403. 5 indexed citations
2.
Žáčeková, Eva, et al.. (2013). Persistent excitation condition within the dual control framework. Journal of Process Control. 23(9). 1270–1280. 20 indexed citations
3.
Váňa, Zdeněk, Samuel Prívara, Eva Žáčeková, & Jiří Cigler. (2013). Building semi-physical modeling: On selection of the model complexity. 3955–3960. 5 indexed citations
4.
Cigler, Jiří, et al.. (2012). Optimization of predicted mean vote thermal comfort index within Model Predictive Control framework. 3056–3061. 11 indexed citations
5.
Prívara, Samuel, Jiří Cigler, Zdeněk Váňa, et al.. (2012). Building modeling as a crucial part for building predictive control. Energy and Buildings. 56. 8–22. 289 indexed citations
6.
Žáčeková, Eva, Samuel Prívara, & Josef Komárek. (2012). On dual control for buildings using persistent excitation condition. 2158–2163. 2 indexed citations
7.
Prívara, Samuel, et al.. (2012). Incorporation of system steady state properties into subspace identification algorithm. International Journal of Modelling Identification and Control. 16(2). 159–159. 3 indexed citations
8.
Prívara, Samuel, Jiří Cigler, Zdeněk Váňa, Frauke Oldewurtel, & Eva Žáčeková. (2012). Use of partial least squares within the control relevant identification for buildings. Control Engineering Practice. 21(1). 113–121. 32 indexed citations
9.
Prívara, Samuel, Zdeněk Váňa, Eva Žáčeková, & Jiří Cigler. (2012). Building modeling: Selection of the most appropriate model for predictive control. Energy and Buildings. 55. 341–350. 79 indexed citations
10.
Žáčeková, Eva & Samuel Prívara. (2012). Control relevant identification and predictive control of a building. 246–251. 11 indexed citations
11.
Cigler, Jiří, Samuel Prívara, Zdeněk Váňa, Eva Žáčeková, & Lukáš Ferkl. (2012). Optimization of Predicted Mean Vote index within Model Predictive Control framework: Computationally tractable solution. Energy and Buildings. 52. 39–49. 87 indexed citations
12.
Prívara, Samuel, Zdeněk Váňa, Jiří Cigler, & Lukáš Ferkl. (2012). Predictive control oriented subspace identification based on building energy simulation tools. 3. 1290–1295. 7 indexed citations
13.
Žáčeková, Eva, Samuel Prívara, & Zdeněk Váňa. (2011). Model predictive control relevant identification using partial least squares for building modeling. 422–427. 22 indexed citations
14.
Široký, Ján, Frauke Oldewurtel, Jiří Cigler, & Samuel Prívara. (2011). Experimental analysis of model predictive control for an energy efficient building heating system. Applied Energy. 88(9). 3079–3087. 614 indexed citations breakdown →
15.
Prívara, Samuel, Zdeněk Váňa, Dimitrios Gyalistras, et al.. (2011). Modeling and identification of a large multi-zone office building. 55–60. 60 indexed citations
16.
Cigler, Jiří & Samuel Prívara. (2010). Subspace identification and model predictive control for buildings. 750–755. 33 indexed citations
17.
Prívara, Samuel, Jiří Cigler, Zdeněk Váňa, Lukáš Ferkl, & Michael Šebek. (2010). Subspace identification of poorly excited industrial systems. 4405–4410. 17 indexed citations
18.
Prívara, Samuel, Ján Široký, Lukáš Ferkl, & Jiří Cigler. (2010). Model predictive control of a building heating system: The first experience. Energy and Buildings. 43(2-3). 564–572. 367 indexed citations breakdown →
19.
Ferkl, Lukáš, Ján Široký, & Samuel Prívara. (2010). Model predictive control of buildings: The efficient way of heating. 1. 1922–1926. 9 indexed citations
20.
Prívara, Samuel, et al.. (2010). Probabilistic risk assessment of highway tunnels. Tunnelling and Underground Space Technology. 26(1). 71–82. 44 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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