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

Iván de-Paz-Centeno, María Teresa García-Ordás, Oscar García-Olalla, Javier Arenas and Héctor Alaiz-Moretón
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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

Energies, 2021, vol. 14, issue 16, 1-14

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.

Keywords: super resolution perception; super resolution of energy; data interpolation; convolutional neural network; deep-learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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