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M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption Environments

Author

Listed:
  • Iván de-Paz-Centeno

    (SMARKIA ENERGY S.L., Av. Padre Isla 16, 24002 León, Spain)

  • María Teresa García-Ordás

    (SECOMUCI Research Groups, Escuela de Ingenierías Industrial e Informática, Universidad de León, Campus de Vegazana s/n, 24071 León, Spain)

  • Oscar García-Olalla

    (SMARKIA ENERGY S.L., Av. Padre Isla 16, 24002 León, Spain)

  • Javier Arenas

    (SMARKIA ENERGY S.L., Av. Padre Isla 16, 24002 León, Spain)

  • Héctor Alaiz-Moretón

    (SECOMUCI Research Groups, Escuela de Ingenierías Industrial e Informática, Universidad de León, Campus de Vegazana s/n, 24071 León, Spain)

Abstract

We propose M-SRPCNN, a fully convolutional generative deep neural network to recover missing historical hourly data from a sensor based on the historic monthly energy consumption. The network performs a reconstruction of the load profile while keeping the overall monthly consumption, which makes it suitable to effectively replace energy apportioning systems. Experiments demonstrate that M-SRPCNN can effectively reconstruct load curves from single month overall values, outperforming traditional apportioning systems.

Suggested Citation

  • Iván de-Paz-Centeno & María Teresa García-Ordás & Oscar García-Olalla & Javier Arenas & Héctor Alaiz-Moretón, 2021. "M-SRPCNN: A Fully Convolutional Neural Network Approach for Handling Super Resolution Reconstruction on Monthly Energy Consumption Environments," Energies, MDPI, vol. 14(16), pages 1-14, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4765-:d:609233
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