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A TensorFlow Approach to Data Analysis for Time Series Forecasting in the Energy-Efficiency Realm

Author

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  • J. R. S. Iruela

    (Department of Computer Science and Artificial Intelligence, Calle Periodista Daniel Saucedo Aranda, s/n, 18014 Granada, Spain)

  • L. G. B. Ruiz

    (Department of Computer Science and Artificial Intelligence, Calle Periodista Daniel Saucedo Aranda, s/n, 18014 Granada, Spain)

  • M. I. Capel

    (Department of Software Engineering, Calle Periodista Daniel Saucedo Aranda, s/n, 18014 Granada, Spain)

  • M. C. Pegalajar

    (Department of Computer Science and Artificial Intelligence, Calle Periodista Daniel Saucedo Aranda, s/n, 18014 Granada, Spain)

Abstract

Thanks to advances in smart metering devices (SM), the electricity sector is undergoing a series of changes, among which it is worth highlighting the ability to control the response to all events that occur in the electricity grid with the intention of making it more smart. Predicting electricity consumption data is a key factor for the energy sector in order to create a completely intelligent electricity grid that optimizes consumption and forecasts future energy needs. However, it is currently not enough to give a prediction of energy consumption (EC), but it is also necessary to give the prediction as fast as possible so that the grid can operate in the shortest possible time. An approach for developing EC prediction systems is introduced here by the use of artificial neural networks (ANN). Differently from other research studies on the subject, a divide-and-conquer strategy is used so that the target system’s execution switches from one to another specialized small models that forecast the EC of a building within the time range of one hour. By simultaneously processing a large amount of data and models, a consequence of implementing them in parallel with TensorFlow on GPUs, the training procedure proposed here increases the performance of the classic time series prediction methods, which are based on ANN. Leveraging the latest generation of ANN techniques and new GPU-based architectures, correct EC predictions can be obtained and, as the experimentation carried out in this work shows, such predictions can be obtained quickly. The obtained results in this study show a promising way for speeding up big data processing of building’s monitoring data to achieve energy efficiency.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:4038-:d:588390
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    References listed on IDEAS

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    Cited by:

    1. M. C. Pegalajar & L. G. B. Ruiz, 2022. "Time Series Forecasting for Energy Consumption," Energies, MDPI, vol. 15(3), pages 1-3, January.
    2. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    3. Manuel I. Capel, 2022. "Artificial Neuron-Based Model for a Hybrid Real-Time System: Induction Motor Case Study," Mathematics, MDPI, vol. 10(18), pages 1-30, September.
    4. L. Cabezón & L. G. B. Ruiz & D. Criado-Ramón & E. J. Gago & M. C. Pegalajar, 2022. "Photovoltaic Energy Production Forecasting through Machine Learning Methods: A Scottish Solar Farm Case Study," Energies, MDPI, vol. 15(22), pages 1-14, November.
    5. Luzia, Ruan & Rubio, Lihki & Velasquez, Carlos E., 2023. "Sensitivity analysis for forecasting Brazilian electricity demand using artificial neural networks and hybrid models based on Autoregressive Integrated Moving Average," Energy, Elsevier, vol. 274(C).

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