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

Author

Listed:
  • 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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    1. Dudek, Grzegorz, 2016. "Multilayer perceptron for GEFCom2014 probabilistic electricity price forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1057-1060.
    2. Antonio Paone & Jean-Philippe Bacher, 2018. "The Impact of Building Occupant Behavior on Energy Efficiency and Methods to Influence It: A Review of the State of the Art," Energies, MDPI, vol. 11(4), pages 1-19, April.
    3. Rubén Pérez-Chacón & José M. Luna-Romera & Alicia Troncoso & Francisco Martínez-Álvarez & José C. Riquelme, 2018. "Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities," Energies, MDPI, vol. 11(3), pages 1-19, March.
    4. Beccali, Marco & Ciulla, Giuseppina & Lo Brano, Valerio & Galatioto, Alessandra & Bonomolo, Marina, 2017. "Artificial neural network decision support tool for assessment of the energy performance and the refurbishment actions for the non-residential building stock in Southern Italy," Energy, Elsevier, vol. 137(C), pages 1201-1218.
    5. Wilson Castro & Jimy Oblitas & Roberto Santa-Cruz & Himer Avila-George, 2017. "Multilayer perceptron architecture optimization using parallel computing techniques," PLOS ONE, Public Library of Science, vol. 12(12), pages 1-17, December.
    6. Francisco Javier Duque-Pintor & Manuel Jesús Fernández-Gómez & Alicia Troncoso & Francisco Martínez-Álvarez, 2016. "A New Methodology Based on Imbalanced Classification for Predicting Outliers in Electricity Demand Time Series," Energies, MDPI, vol. 9(9), pages 1-10, September.
    7. Vangelis Marinakis, 2020. "Big Data for Energy Management and Energy-Efficient Buildings," Energies, MDPI, vol. 13(7), pages 1-18, March.
    8. Luis Gonzaga Baca Ruiz & Manuel Pegalajar Cuéllar & Miguel Delgado Calvo-Flores & María Del Carmen Pegalajar Jiménez, 2016. "An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings," Energies, MDPI, vol. 9(9), pages 1-21, August.
    9. Claudia Aravena & Andrés Riquelme & Eleanor Denny, 2016. "Money, Comfort or Environment? Priorities and Determinants of Energy Efficiency Investments in Irish Households," Journal of Consumer Policy, Springer, vol. 39(2), pages 159-186, June.
    10. Nejat, Payam & Jomehzadeh, Fatemeh & Taheri, Mohammad Mahdi & Gohari, Mohammad & Abd. Majid, Muhd Zaimi, 2015. "A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 843-862.
    11. Ahmadi-Karvigh, Simin & Ghahramani, Ali & Becerik-Gerber, Burcin & Soibelman, Lucio, 2018. "Real-time activity recognition for energy efficiency in buildings," Applied Energy, Elsevier, vol. 211(C), pages 146-160.
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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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