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Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers

Author

Listed:
  • Alexandru Pîrjan

    (Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, Bucharest 012101, Romania)

  • Simona-Vasilica Oprea

    (Department of Economic Informatics and Cybernetics, The Bucharest Academy of Economic Studies, Romana Square 6, Bucharest 010374, Romania)

  • George Căruțașu

    (Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, Bucharest 012101, Romania)

  • Dana-Mihaela Petroșanu

    (Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, Bucharest 012101, Romania
    Department of Mathematics-Informatics, University Politehnica of Bucharest, Splaiul Independenței 313, Bucharest 060042, Romania)

  • Adela Bâra

    (Department of Economic Informatics and Cybernetics, The Bucharest Academy of Economic Studies, Romana Square 6, Bucharest 010374, Romania)

  • Cristina Coculescu

    (Department of Informatics, Statistics and Mathematics, Romanian-American University, Expoziției 1B, Bucharest 012101, Romania)

Abstract

This paper focuses on an important issue regarding the forecasting of the hourly energy consumption in the case of large electricity non-household consumers that account for a significant percentage of the whole electricity consumption, the accurate forecasting being a key-factor in achieving energy efficiency. In order to devise the forecasting solutions, we have developed a series of dynamic neural networks for solving nonlinear time series problems, based on the non-linear autoregressive (NAR) and non-linear autoregressive with exogenous inputs (NARX) models. In both cases, we have used large datasets comprising the hourly energy consumption recorded by the smart metering device from a commercial center type of consumer (a large hypermarket), while in the NARX case we have used supplementary temperature and time stamps datasets. Of particular interest was to research and obtain an optimal mix between the training algorithm (Levenberg-Marquardt, Bayesian Regularization, Scaled Conjugate Gradient), the hidden number of neurons and the delay parameter. Using performance metrics and forecasting scenarios, we have obtained results that highlight an increased accuracy of the developed forecasting solutions. The developed hourly consumption forecasting solutions can bring significant benefits to both the consumers and electricity suppliers.

Suggested Citation

  • Alexandru Pîrjan & Simona-Vasilica Oprea & George Căruțașu & Dana-Mihaela Petroșanu & Adela Bâra & Cristina Coculescu, 2017. "Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers," Energies, MDPI, vol. 10(11), pages 1-36, October.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:11:p:1727-:d:116733
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    References listed on IDEAS

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    Cited by:

    1. Simona-Vasilica Oprea & Adela Bâra & Adriana Reveiu, 2018. "Informatics Solution for Energy Efficiency Improvement and Consumption Management of Householders," Energies, MDPI, vol. 11(1), pages 1-31, January.
    2. Anam-Nawaz Khan & Naeem Iqbal & Atif Rizwan & Rashid Ahmad & Do-Hyeun Kim, 2021. "An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings," Energies, MDPI, vol. 14(11), pages 1-25, May.
    3. Alexandru Pîrjan & George Căruțașu & Dana-Mihaela Petroșanu, 2018. "Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain," Energies, MDPI, vol. 11(10), pages 1-42, October.
    4. Omar Fetitah & Mohammed Kadi Attouch & Salah Khardani & Ali Righi, 2023. "Robust nonparametric equivariant regression for functional data with responses missing at random," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(8), pages 899-929, November.
    5. Simona-Vasilica Oprea & Adela Bâra & Adina Ileana Uță & Alexandru Pîrjan & George Căruțașu, 2018. "Analyses of Distributed Generation and Storage Effect on the Electricity Consumption Curve in the Smart Grid Context," Sustainability, MDPI, vol. 10(7), pages 1-25, July.
    6. Dana-Mihaela Petroșanu & Alexandru Pîrjan, 2020. "Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network," Sustainability, MDPI, vol. 13(1), pages 1-31, December.
    7. Federico Divina & Aude Gilson & Francisco Goméz-Vela & Miguel García Torres & José F. Torres, 2018. "Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting," Energies, MDPI, vol. 11(4), pages 1-31, April.
    8. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.

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