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Predicting the Future-Big Data and Machine Learning

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  • Fernando Sánchez Lasheras

    (Department of Mathematics, University of Oviedo, 33007 Oviedo, Spain
    Instituto Universitario de Ciencias y Tecnologías Espaciales de Asturias (ICTEA), University of Oviedo, 33004 Oviedo, Spain)

Abstract

In recent decades, due to the increase in the capabilities of microprocessors and the advent of graphics processing units (GPUs), the use of machine learning methodologies has become popular in many fields of science and technology [...]

Suggested Citation

  • Fernando Sánchez Lasheras, 2021. "Predicting the Future-Big Data and Machine Learning," Energies, MDPI, vol. 14(23), pages 1-2, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8041-:d:692911
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    References listed on IDEAS

    as
    1. Aroa González Fuentes & Nélida M. Busto Serrano & Fernando Sánchez Lasheras & Gregorio Fidalgo Valverde & Ana Suárez Sánchez, 2020. "Prediction of Health-Related Leave Days among Workers in the Energy Sector by Means of Genetic Algorithms," Energies, MDPI, vol. 13(10), pages 1-16, May.
    2. Nanyan Zhu & Chen Liu & Andrew F. Laine & Jia Guo, 2020. "Understanding and Modeling Climate Impacts on Photosynthetic Dynamics with FLUXNET Data and Neural Networks," Energies, MDPI, vol. 13(6), pages 1-11, March.
    3. Ruijin Zhu & Weilin Guo & Xuejiao Gong, 2019. "Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning," Energies, MDPI, vol. 12(17), pages 1-18, August.
    4. Beatriz M. Paredes-Sánchez & José P. Paredes-Sánchez & Paulino J. García-Nieto, 2020. "Energy Multiphase Model for Biocoal Conversion Systems by Means of a Nodal Network," Energies, MDPI, vol. 13(11), pages 1-13, May.
    5. Hualing Lin & Qiubi Sun, 2020. "Crude Oil Prices Forecasting: An Approach of Using CEEMDAN-Based Multi-Layer Gated Recurrent Unit Networks," Energies, MDPI, vol. 13(7), pages 1-21, March.
    6. Cristina Puente & Rafael Palacios & Yolanda González-Arechavala & Eugenio Francisco Sánchez-Úbeda, 2020. "Non-Intrusive Load Monitoring (NILM) for Energy Disaggregation Using Soft Computing Techniques," Energies, MDPI, vol. 13(12), pages 1-20, June.
    7. Marta Matyjaszek & Gregorio Fidalgo Valverde & Alicja Krzemień & Krzysztof Wodarski & Pedro Riesgo Fernández, 2020. "Optimizing Predictor Variables in Artificial Neural Networks When Forecasting Raw Material Prices for Energy Production," Energies, MDPI, vol. 13(8), pages 1-15, April.
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