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Time Series Forecasting for Energy Consumption

Author

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  • M. C. Pegalajar

    (Department of Computer Science and Artificial Intelligence, University of Granada, 18014 Granada, Spain)

  • L. G. B. Ruiz

    (Department of Software Engineering, University of Granada, 18014 Granada, Spain)

Abstract

Introduction In the last few years, there has been considerable progress in time series forecasting algorithms, which are becoming more and more accurate, and their applications are numerous and varied [...]

Suggested Citation

  • M. C. Pegalajar & L. G. B. Ruiz, 2022. "Time Series Forecasting for Energy Consumption," Energies, MDPI, vol. 15(3), pages 1-3, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:773-:d:730180
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    References listed on IDEAS

    as
    1. Daniel Ramos & Pedro Faria & Zita Vale & João Mourinho & Regina Correia, 2020. "Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning," Energies, MDPI, vol. 13(18), pages 1-18, September.
    2. Fermín Rodríguez & Fernando Martín & Luis Fontán & Ainhoa Galarza, 2020. "Very Short-Term Load Forecaster Based on a Neural Network Technique for Smart Grid Control," Energies, MDPI, vol. 13(19), pages 1-19, October.
    3. Shahrooz Abghari & Veselka Boeva & Jens Brage & Håkan Grahn, 2020. "A Higher Order Mining Approach for the Analysis of Real-World Datasets," Energies, MDPI, vol. 13(21), pages 1-23, November.
    4. Seok-Jun Bu & Sung-Bae Cho, 2020. "Time Series Forecasting with Multi-Headed Attention-Based Deep Learning for Residential Energy Consumption," Energies, MDPI, vol. 13(18), pages 1-16, September.
    5. J. R. S. Iruela & L. G. B. Ruiz & M. I. Capel & M. C. Pegalajar, 2021. "A TensorFlow Approach to Data Analysis for Time Series Forecasting in the Energy-Efficiency Realm," Energies, MDPI, vol. 14(13), pages 1-22, July.
    Full references (including those not matched with items on IDEAS)

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