Publications dans lesquelles il/elle collabore avec José Cristóbal Riquelme Santos (42)

2024

  1. Preface

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  2. Técnicas Big Data para la predicción de la demanda y precio eléctrico

    Economía industrial, Núm. 431, pp. 119-130

2021

  1. Preface

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2020

  1. Coronavirus Optimization Algorithm: A Bioinspired Metaheuristic Based on the COVID-19 Propagation Model

    Big data, Vol. 8, Núm. 4, pp. 308-322

2019

  1. A novel ensemble method for electric vehicle power consumption forecasting: Application to the Spanish system

    IEEE Access, Vol. 7, pp. 120840-120856

  2. Impact of Auto-evaluation Tests as Part of the Continuous Evaluation in Programming Courses

    Advances in Intelligent Systems and Computing

2018

  1. Big data analytics for discovering electricity consumption patterns in smart cities

    Energies, Vol. 11, Núm. 3

  2. Data science and big data in energy forecasting

    Energies, Vol. 11, Núm. 11

  3. SmartFD: A real big data application for electrical fraud detection

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

2017

  1. Application of the Weighted Nearest Neighbor Method to Power System Forecasting Problems

    ADVANCES IN ELECTRIC POWER AND ENERGY SYSTEMS: Load and Price Forecasting (wiley), pp. 41-88

  2. Applications of Computational Intelligence in Time Series

    Computational Intelligence and Neuroscience

  3. Recent advances in energy time series forecasting

    Energies

2016

  1. Improving a multi-objective evolutionary algorithm to discover quantitative association rules

    Knowledge and Information Systems, Vol. 49, Núm. 2, pp. 481-509

  2. Obtaining optimal quality measures for quantitative association rules

    Neurocomputing, Vol. 176, pp. 36-47

2015

  1. A comparison of machine learning regression techniques for LiDAR-derived estimation of forest variables

    Neurocomputing, Vol. 167, pp. 24-31

  2. A multi-scale smoothing kernel for measuring time-series similarity

    Neurocomputing, Vol. 167, pp. 8-17

  3. A survey on data mining techniques applied to electricity-related time series forecasting

    Energies, Vol. 8, Núm. 11, pp. 13162-13193

  4. Enhancing the scalability of a genetic algorithm to discover quantitative association rules in large-scale datasets

    Integrated Computer-Aided Engineering, Vol. 22, Núm. 1, pp. 21-39

  5. Local models-based regression trees for very short-term wind speed prediction

    Renewable Energy, Vol. 81, pp. 589-598

2014

  1. A comparative study of machine learning regression methods on LiDAR data: A case study

    Advances in Intelligent Systems and Computing