JOSE PABLO PAREDES SANCHEZ-rekin lankidetzan egindako argitalpenak (11)

2024

  1. Estimation of the coal higher heating value for energy systems relied on ultimate analysis with machine learning techniques

    Fuel, Vol. 357

2023

  1. Modelling energy performance of residential dwellings by using the MARS technique, SVM-based approach, MLP neural network and M5 model tree

    Applied Energy, Vol. 341

  2. Modelling hydrogen production from biomass pyrolysis for energy systems using machine learning techniques

    Environmental Science and Pollution Research, Vol. 30, Núm. 31, pp. 76977-76991

2022

  1. Evaluation of implementation of biomass and solar resources by energy systems in the coal-mining areas of spain

    Energies, Vol. 15, Núm. 1

  2. Forecast of the higher heating value based on proximate analysis by using support vector machines and multilayer perceptron in bioenergy resources

    Fuel, Vol. 317

2021

  1. A new hybrid model to foretell thermal power efficiency from energy performance certificates at residential dwellings applying a Gaussian process regression

    Neural Computing and Applications, Vol. 33, Núm. 12, pp. 6627-6640

  2. Modelling energy performance using a new hybrid DE/MARS–based approach for fossil-fuel thermal power stations

    Environmental Science and Pollution Research, Vol. 28, Núm. 4, pp. 4417-4429

  3. Prediction of the critical temperature of a superconductor by using the WOA/MARS, Ridge, Lasso and Elastic-net machine learning techniques

    Neural Computing and Applications, Vol. 33, Núm. 24, pp. 17131-17145

2020

  1. Energy multiphase model for biocoal conversion systems by means of a nodal network

    Energies, Vol. 13, Núm. 11

2019

  1. Forecast of the higher heating value in biomass torrefaction by means of machine learning techniques

    Journal of Computational and Applied Mathematics, Vol. 357, pp. 284-301

  2. Predictive modelling of the higher heating value in biomass torrefaction for the energy treatment process using machine-learning techniques

    Neural Computing and Applications, Vol. 31, Núm. 12, pp. 8823-8836