sample of publications

• articles 

  • Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques.  Remote Sensing.  15:2328. 2023
  • Pareto optimal prediction intervals with hypernetworks.  APPLIED SOFT COMPUTING.  133:1-14. 2023
  • A combination of supervised dimensionality reduction and learning methods to forecast solar radiation.  APPLIED INTELLIGENCE.  53:13053-13066. 2022
  • Direct estimation of prediction intervals for solar and wind regional energy forecasting with deep neural networks.  ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE.  114. 2022
  • Deep neural networks for the quantile estimation of regional renewable energy production.  APPLIED INTELLIGENCE.  53:8318-8353. 2022
  • Evolutionary-based prediction interval estimation by blending solar radiation forecasting models using meteorological weather types.  APPLIED SOFT COMPUTING.  109:1-13. 2021
  • Using a Multi-view Convolutional Neural Network to monitor solar irradiance.  NEURAL COMPUTING & APPLICATIONS.  1-13. 2021
  • Supervised data transformation and dimensionality reduction with a 3-layer multi-layer perceptron for classification problems.  Journal of Ambient Intelligence and Humanized Computing.  12:10515-10527. 2021
  • A short-term solar radiation forecasting system for the Iberian Peninsula. Part 1: Models description and performance assessment.  Solar Energy.  195:396-412. 2020
  • A short-term solar radiation forecasting system for the Iberian Peninsula. Part 2: Model blending approaches based on machine learning.  Solar Energy.  195:685-696. 2020
  • Improving prediction intervals using measured solar power with a multi-objective approach.  Energies.  12:4713. 2019
  • A Comparative Study of Classical Clustering Method and Cuckoo Search Approach for Satellite Image Clustering: Application to Water Body Extraction.  APPLIED ARTIFICIAL INTELLIGENCE.  32:96-118. 2018
  • A filter attribute selection method based on local reliable information.  APPLIED INTELLIGENCE.  48:35-45. 2018
  • Multi-objective evolutionary optimization of prediction intervals for solar energy forecasting with neural networks.  INFORMATION SCIENCES.  418:363-382. 2017
  • Automatic Cloud-Type Classification Based On the Combined Use of a Sky Camera and a Ceilometer.  JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES.  122:11,045-11,061. 2017
  • Improving the separation of direct and diffuse solar radiation components using machine learning by gradient boosting.  Solar Energy.  150:558-569. 2017
  • Machine learning techniques for daily solar energy prediction and interpolation using numerical weather models.  CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE.  28:1261-1274. 2016
  • Optimizing the number of electrodes and spatial filters for Brain-Computer Interfaces by means of an evolutionary multi-objective approach.  EXPERT SYSTEMS WITH APPLICATIONS.  42:6215-6223. 2015
  • Temperature profile retrieval in axisymmetric combustion plumes using multilayer perceptron modeling and spectral feature selection in the infrared CO2 emission band.  APPLIED SPECTROSCOPY.  68:900-908. 2014
  • Extended mean-variance model for reliable evolutionary portfolio optimization.  AI COMMUNICATIONS.  27:315-324. 2014
  • Multiobjective algorithms with resampling for portfolio optimization.  COMPUTING AND INFORMATICS.  32:777-796. 2013
  • Applying evolution strategies to preprocessing EEG signals for brain-computer interfaces.  INFORMATION SCIENCES.  215:53-56. 2012
  • Time-stamped resampling for robust evolutionary portfolio optimization.  EXPERT SYSTEMS WITH APPLICATIONS.  39:10722-10730. 2012
  • Multi-objective metaheuristics for preprocessing EEG data in brain-computer interfaces.  ENGINEERING OPTIMIZATION.  44:373-390. 2012
  • A lazy learning approach for building classification models.  INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS.  26:773-786. 2011
  • Recursive discriminant regression analysis to find homogeneous groups.  International Journal of Neural Systems.  21:21-95. 2011
  • AMPSO: A New Particle Swarm Method for Nearest Neighborhood Classification.  IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS (IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND RE).  39:1082-1091. 2009
  • Michigan Particle Swarm Optimization for Prototype Reduction in Classification Problems.  NEW GENERATION COMPUTING.  27:239-257. 2009
  • Learning Radial Basis Neural Networks in a Lazy Way: A Comparative Study.  NEUROCOMPUTING.  71:2529-2537. 2008
  • Multilayer Perceptron as Inverse Model in a Ground-Based Remote Sensing Temperature Retrieval Problem.  ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE.  21:26-34. 2008

• book chapters 

  • Transition Detection for Brain Computer Interface Classification. In: Biomedical Engineering Systems and Technologies: International Joint Conference, BIOSTEC 2009 Porto, Portugal, January 14-17, 2009, Revised Selected Papers.  SPRINGER. 200-210. 2010
  • Supervised Clustering via Principal Component Analysis in a Retrieval Application. In: Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Dat: (SensorKDD'09): held in conjunction with KDD-09, June 28, 2009, Paris, France.  Association for computer Machinery (ACM). 97-104. 2009
  • Neural Lazy Local Learning. In: Optimization Techniques for Solving Complex Problems.  WILEY. 2009

• conference contributions 

  • Predicting Global Irradiance Combining Forecasting Models Through Machine Learning.  622-633. 2018
  • Studying the Effect of Measured Solar Power on Evolutionary Multi-objective Prediction Intervals.  155-162. 2018
  • Wind Energy Forecasting at Different Time Horizons with Individual and Global Models.  240-248. 2018
  • A Study of Machine Learning Techniques for Daily Solar Energy Forecasting using Numerical Weather Models.  269-278. 2015
  • Interpolación espacial para la predicción de la radiación solar mediante gradient tree boosting 2015
  • Portfolio Optimization Using SPEA2 with Resampling.  127-134. 2011
  • A Study of the Noise Sensitivity of a Particle Swarm Algorithm for Classification 2008