Biography:
The researcher José Portela studied in the Universidad Pontificia Comillas where he obtained the degree Ingeniero Industrial in Electrical Engineering. In 2011 he joined the Institute for Research in Technology (IIT) as a resereach member. His areas of interest include time series models, Functional Data Analysis and Neural Networks with applications to the electricity markets.
Areas of interest:
Functional Data Analysis, Machine Learning, Neural Networks, time series models
Skills:
Data Analysis, Machine Learning, Matlab, R, Python, Hidden Markov Models Decision trees, Unsupervised machine learning: Clustering Supervised machine learning: neural networks, random forests, gradient booster, Physics Informed Neural Networks (PINN)
Portela, J., Roch Dupré, D., Figuerola-Ferretti Garrigues, I., Yéboles, C., Salazar, A., Monitoring the green transition in the power sector with the electricity generation emissions (EGE) tracker. Energy Strategy Reviews. Vol. 50, pp. 101236-1 - 101236-15, November 2023. [Online: October 2023] 
JCR: 7,900 Q1 (2023); - SJR: 1,899 Q1 (2023);
JCR: 4,500 Q1 (2022); 4,800 Q1 (2023) - SJR: 0,929 Q1 (2022); 1,093 Q1 (2023)
JCR: 5,400 Q1 (2022); 4,800 Q1 (2023) - SJR: 1,225 Q1 (2022); 1,232 Q1 (2023) Pizarroso, J., Portela, J., Muñoz, A., NeuralSens: sensitivity analysis of neural networks. Journal of Statistical Software. Vol. 102, nº. 7, pp. 1 - 36, April 2022.
JCR: 5,800 Q1 (2022); 5,400 Q1 (2023) - SJR: 2,722 Q1 (2022); 2,709 Q1 (2023)
JCR: 2,035 Q2 (2021); 1,500 Q2 (2023) - SJR: 1,016 Q1 (2021); 1,008 Q1 (2023)
JCR: 4,630 Q1 (2020); 5,000 Q1 (2023) - SJR: 1,050 Q1 (2020); 1,711 Q1 (2023)
JCR: 6,807 Q1 (2018); 6,500 Q1 (2023) - SJR: Q1 (2018); 3,827 Q1 (2023)
JCR: 5,255 Q1 (2017); 6,500 Q1 (2023) - SJR: Q1 (2017); 3,827 Q1 (2023)
JCR: 5,680 Q1 (2016); 6,500 Q1 (2023) - SJR: Q1 (2016); 3,827 Q1 (2023) 
