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MORED: A Moroccan Buildings’ Electricity Consumption Dataset

Author

Listed:
  • Mohamed Aymane Ahajjam

    (TICLab, International University of Rabat, Rabat 11100, Morocco
    ENSIAS, Mohammed V University in Rabat, Rabat 10000, Morocco)

  • Daniel Bonilla Licea

    (Faculty of Electrical Engineering, Czech Technical University, 166 36 Prague, Czech Republic)

  • Chaimaa Essayeh

    (TICLab, International University of Rabat, Rabat 11100, Morocco)

  • Mounir Ghogho

    (TICLab, International University of Rabat, Rabat 11100, Morocco
    School of EEE, University of Leeds, Leeds LS2 9JT, UK)

  • Abdellatif Kobbane

    (ENSIAS, Mohammed V University in Rabat, Rabat 10000, Morocco)

Abstract

This paper consists of two parts: an overview of existing open datasets of electricity consumption and a description of the Moroccan Buildings’ Electricity Consumption Dataset, a first of its kind, coined as MORED. The new dataset comprises electricity consumption data of various Moroccan premises. Unlike existing datasets, MORED provides three main data components: whole premises (WP) electricity consumption, individual load (IL) ground-truth consumption, and fully labeled IL signatures, from affluent and disadvantaged neighborhoods. The WP consumption data were acquired at low rates (1/5 or 1/10 samples/s) from 12 households; the IL ground-truth data were acquired at similar rates from five households for extended durations; and IL signature data were acquired at high and low rates (50 k and 4 samples/s) from 37 different residential and industrial loads. In addition, the dataset encompasses non-intrusive load monitoring (NILM) metadata.

Suggested Citation

  • Mohamed Aymane Ahajjam & Daniel Bonilla Licea & Chaimaa Essayeh & Mounir Ghogho & Abdellatif Kobbane, 2020. "MORED: A Moroccan Buildings’ Electricity Consumption Dataset," Energies, MDPI, vol. 13(24), pages 1-22, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:24:p:6737-:d:465525
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    References listed on IDEAS

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    1. Carrie Armel, K. & Gupta, Abhay & Shrimali, Gireesh & Albert, Adrian, 2013. "Is disaggregation the holy grail of energy efficiency? The case of electricity," Energy Policy, Elsevier, vol. 52(C), pages 213-234.
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    Cited by:

    1. Fang, Lei & He, Bin, 2023. "A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting," Applied Energy, Elsevier, vol. 348(C).
    2. Ahajjam, Mohamed Aymane & Bonilla Licea, Daniel & Ghogho, Mounir & Kobbane, Abdellatif, 2022. "Experimental investigation of variational mode decomposition and deep learning for short-term multi-horizon residential electric load forecasting," Applied Energy, Elsevier, vol. 326(C).

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