Antonio Sánchez Esguevillas-rekin lankidetzan egindako argitalpenak (15)

2021

  1. Additive ensemble neural network with constrained weighted quantile loss for probabilistic electric‐load forecasting

    Sensors, Vol. 21, Núm. 9

  2. Novel data-driven models applied to short-term electric load forecasting

    Applied Sciences (Switzerland), Vol. 11, Núm. 12

2019

  1. Exploratory study on Class Imbalance and solutions for Network Traffic Classification

    Neurocomputing, Vol. 343, pp. 100-119

2017

  1. Aprendizaje Automático para Caracterización de Tráfico de Redes: Metodología y Comparación de Algoritmos Ensamblados

    III Jornadas Doctorales Escuela Internacional de Doctorado de la Universidad de Murcia (EIDUM)

  2. Ensemble network traffic classification: Algorithm comparison and novel ensemble scheme proposal

    Computer Networks, Vol. 127, pp. 68-80

2014

  1. A survey on electric power demand forecasting: Future trends in smart grids, microgrids and smart buildings

    IEEE Communications Surveys and Tutorials, Vol. 16, Núm. 3, pp. 1460-1495

  2. Artificial neural network for short-term load forecasting in distribution systems

    Energies, Vol. 7, Núm. 3, pp. 1576-1598

  3. Artificial neural networks for short-term load forecasting in microgrids environment

    Energy, Vol. 75, pp. 252-264

2013

  1. A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants

    IEEE Communications Magazine, Vol. 51, Núm. 1, pp. 106-113

  2. Experimental analysis of the input variables' relevance to forecast next day's aggregated electric demand using neural networks

    Energies, Vol. 6, Núm. 6, pp. 2927-2948

  3. Improved short-term load forecasting based on two-stage predictions with artificial neural networks in a microgrid environment

    Energies, Vol. 6, Núm. 9, pp. 4489-4507

  4. Short-term load forecasting for microgrids based on artificial neural networks

    Energies, Vol. 6, Núm. 3, pp. 1385-1408

2012

  1. A study of the relationship between weather variables and electric power demand inside a smart grid/smart world framework

    Sensors (Switzerland), Vol. 12, Núm. 9, pp. 11571-11591

  2. Classification and clustering of electricity demand patterns in industrial parks

    Energies, Vol. 5, Núm. 12, pp. 5215-5228

  3. Performance study of the application of artificial neural networks to the completion and prediction of data retrieved by underwater sensors

    Sensors, Vol. 12, Núm. 2, pp. 1468-1481