C. Casanova‐Mateo

1.9k total citations
42 papers, 1.5k citations indexed

About

C. Casanova‐Mateo is a scholar working on Environmental Engineering, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, C. Casanova‐Mateo has authored 42 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Environmental Engineering, 15 papers in Artificial Intelligence and 15 papers in Electrical and Electronic Engineering. Recurrent topics in C. Casanova‐Mateo's work include Energy Load and Power Forecasting (15 papers), Solar Radiation and Photovoltaics (11 papers) and Air Quality Monitoring and Forecasting (10 papers). C. Casanova‐Mateo is often cited by papers focused on Energy Load and Power Forecasting (15 papers), Solar Radiation and Photovoltaics (11 papers) and Air Quality Monitoring and Forecasting (10 papers). C. Casanova‐Mateo collaborates with scholars based in Spain, United States and Australia. C. Casanova‐Mateo's co-authors include Sancho Salcedo‐Sanz, J. Sanz, L. Cornejo-Bueno, Emilio G. Ortiz‐García, Á. Pastor-Sánchez, Pedro Antonio Gutiérrez, David Casillas-Pérez, César Hervás‐Martínez, S. Jiménez‐Fernández and Javier Del Ser and has published in prestigious journals such as Renewable and Sustainable Energy Reviews, Physics Reports and Atmospheric Environment.

In The Last Decade

C. Casanova‐Mateo

42 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
C. Casanova‐Mateo Spain 22 651 583 464 297 242 42 1.5k
Nawin Raj Australia 24 716 1.1× 719 1.2× 736 1.6× 336 1.1× 461 1.9× 49 1.9k
Athanasios Sfetsos Greece 20 459 0.7× 722 1.2× 404 0.9× 215 0.7× 369 1.5× 86 1.6k
Sujan Ghimire Australia 22 949 1.5× 975 1.7× 431 0.9× 465 1.6× 275 1.1× 42 1.8k
Sudheer Ch India 22 783 1.2× 760 1.3× 917 2.0× 297 1.0× 426 1.8× 31 2.1k
Hamdy K. Elminir Egypt 19 716 1.1× 516 0.9× 437 0.9× 730 2.5× 178 0.7× 51 2.2k
David Casillas-Pérez Spain 22 469 0.7× 603 1.0× 212 0.5× 163 0.5× 116 0.5× 59 1.3k
Jamie M. Bright Australia 21 1.2k 1.9× 500 0.9× 178 0.4× 708 2.4× 438 1.8× 45 1.6k
Rubén Urraca Spain 17 1.4k 2.2× 872 1.5× 191 0.4× 999 3.4× 476 2.0× 38 2.2k
Zhendong Zhang China 17 393 0.6× 795 1.4× 282 0.6× 87 0.3× 120 0.5× 25 1.3k
Matteo De Felice Italy 23 469 0.7× 828 1.4× 121 0.3× 326 1.1× 238 1.0× 52 1.4k

Countries citing papers authored by C. Casanova‐Mateo

Since Specialization
Citations

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

Fields of papers citing papers by C. Casanova‐Mateo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by C. Casanova‐Mateo. 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 C. Casanova‐Mateo. The network helps show where C. Casanova‐Mateo may publish in the future.

Co-authorship network of co-authors of C. Casanova‐Mateo

This figure shows the co-authorship network connecting the top 25 collaborators of C. Casanova‐Mateo. A scholar is included among the top collaborators of C. Casanova‐Mateo 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 C. Casanova‐Mateo. C. Casanova‐Mateo 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.
Pérez‐Aracil, Jorge, et al.. (2025). Hybridizing Machine Learning Algorithms With Numerical Models for Accurate Wind Power Forecasting. Expert Systems. 42(2). 3 indexed citations
2.
Pérez‐Aracil, Jorge, et al.. (2024). A general explicable forecasting framework for weather events based on ordinal classification and inductive rules combined with fuzzy logic. Knowledge-Based Systems. 291. 111556–111556. 9 indexed citations
3.
Huertas‐Tato, Javier, et al.. (2023). Spain on fire: A novel wildfire risk assessment model based on image satellite processing and atmospheric information. Knowledge-Based Systems. 283. 111198–111198. 13 indexed citations
4.
Cornejo-Bueno, L., Jorge Pérez‐Aracil, C. Casanova‐Mateo, J. Sanz, & Sancho Salcedo‐Sanz. (2023). Machine Learning Classification-Regression Schemes for Desert Locust Presence Prediction in Western Africa. SSRN Electronic Journal. 1 indexed citations
5.
Pérez‐Aracil, Jorge, et al.. (2023). Extreme Low-Visibility Events Prediction Based on Inductive and Evolutionary Decision Rules: An Explicability-Based Approach. Atmosphere. 14(3). 542–542. 10 indexed citations
6.
Cornejo-Bueno, L., Jorge Pérez‐Aracil, C. Casanova‐Mateo, J. Sanz, & Sancho Salcedo‐Sanz. (2023). Machine Learning Classification–Regression Schemes for Desert Locust Presence Prediction in Western Africa. Applied Sciences. 13(14). 8266–8266. 6 indexed citations
7.
Ser, Javier Del, David Casillas-Pérez, L. Cornejo-Bueno, et al.. (2022). Randomization-based machine learning in renewable energy prediction problems: Critical literature review, new results and perspectives. Applied Soft Computing. 118. 108526–108526. 40 indexed citations
8.
Salcedo‐Sanz, Sancho, David Casillas-Pérez, Javier Del Ser, et al.. (2022). Persistence in complex systems. Physics Reports. 957. 1–73. 40 indexed citations
9.
Casillas-Pérez, David, et al.. (2020). Analysis and Prediction of Dammed Water Level in a Hydropower Reservoir Using Machine Learning and Persistence-Based Techniques. Water. 12(6). 1528–1528. 34 indexed citations
10.
Guijo-Rubio, David, Antonio M. Durán-Rosal, Pedro Antonio Gutiérrez, et al.. (2020). Evolutionary artificial neural networks for accurate solar radiation prediction. Energy. 210. 118374–118374. 70 indexed citations
11.
Guijo-Rubio, David, Pedro Antonio Gutiérrez, C. Casanova‐Mateo, et al.. (2020). Prediction of convective clouds formation using evolutionary neural computation techniques. Neural Computing and Applications. 32(17). 13917–13929. 2 indexed citations
12.
Guijo-Rubio, David, C. Casanova‐Mateo, J. Sanz, et al.. (2019). Ordinal regression algorithms for the analysis of convective situations over Madrid-Barajas airport. Atmospheric Research. 236. 104798–104798. 14 indexed citations
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15.
Gómez-Verdejo, Vanessa, Manel Martínez‐Ramón, C. Casanova‐Mateo, et al.. (2016). Feature selection in solar radiation prediction using bootstrapped SVRs. 3638–3645. 11 indexed citations
16.
Laña, Ibai, et al.. (2016). The role of local urban traffic and meteorological conditions in air pollution: A data-based case study in Madrid, Spain. Atmospheric Environment. 145. 424–438. 69 indexed citations
17.
Sánchez‐Monedero, Javier, Sancho Salcedo‐Sanz, Pedro Antonio Gutiérrez, C. Casanova‐Mateo, & César Hervás‐Martínez. (2014). Simultaneous modelling of rainfall occurrence and amount using a hierarchical nominal–ordinal support vector classifier. Engineering Applications of Artificial Intelligence. 34. 199–207. 32 indexed citations
18.
Alexandre, Enrique, L. Cuadra, Sancho Salcedo‐Sanz, Á. Pastor-Sánchez, & C. Casanova‐Mateo. (2014). Hybridizing Extreme Learning Machines and Genetic Algorithms to select acoustic features in vehicle classification applications. Neurocomputing. 152. 58–68. 47 indexed citations
19.
Salcedo‐Sanz, Sancho, C. Casanova‐Mateo, Jordi Muñoz-Marı́, & Gustau Camps‐Valls. (2014). Prediction of Daily Global Solar Irradiation Using Temporal Gaussian Processes. IEEE Geoscience and Remote Sensing Letters. 11(11). 1936–1940. 73 indexed citations
20.
Salcedo‐Sanz, Sancho, et al.. (2014). Daily global solar radiation prediction based on a hybrid Coral Reefs Optimization – Extreme Learning Machine approach. Solar Energy. 105. 91–98. 144 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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2026