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

Centeno, Iván de Paz; Garcí­a-Ordás, María Teresa; García-Olalla, Oscar; Pinzón-Arenas, Javier Orlando; Aláiz‐Moretón, Héctor

doi:10.3390/en14164765

Cited by 6 publications

(2 citation statements)

References 27 publications

(34 reference statements)

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“…Generative Adversarial Networks (GAN) are a recent family of deep learning methods that perform highly in super-resolution and time series forecasting tasks. The following research papers (Li et al, 2020;De-Paz-centeno et al, 2021;Zhang et al, 2021) proposed GAN-based methods for smart meter's super-resolution problem.…”

Section: Machine Learning-based Methodsmentioning

confidence: 99%

Advancements in super-resolution methods for smart meter data

Iversen,

Khan,

Miraki

et al. 2023

Front. Energy Res.

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This paper presents a comprehensive review of super-resolution methods for smart meter data analysis. Smart meters provide valuable insights into household electricity consumption, but their low-frequency data limits the ability to capture detailed patterns. Super-resolution techniques address this challenge through the reconstruction of high-resolution data from low-resolution measurements. The review covers both non-machine learning-based methods (interpolation, signal processing, and statistics) and machine learning-based methods (CNNs, GANs). Four selected methods are discussed in detail, highlighting their principles, advantages, and limitations. These methods demonstrate superior accuracy in enhancing data completeness, capturing complex relationships, and improving resolution. The review contributes to the advancement of super-resolution techniques for smart meter data analysis, providing researchers and practitioners with valuable insights for efficient energy management and forecasting.

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Section: Machine Learning-based Methodsmentioning

confidence: 99%

Advancements in super-resolution methods for smart meter data

Iversen,

Khan,

Miraki

et al. 2023

Front. Energy Res.

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“…A super-resolution perception convolutional neural network (SRPCNN) is proposed in the study by Liu et al (2020) to generate high-frequency load data from low-frequency data collected by smart meters. A monthlysuper-resolution perception convolutional neural network (M-SRPCNN) is proposed in the study by de-Paz-Centeno et al (2021) to up-sample monthly energy consumption measured at hourly resolution. Compared with other data quality improvement methods, the SRP method has the advantages of higher efficiency, better quality, and richer information.…”

Section: Introductionmentioning

confidence: 99%

Super-resolution perception for wind power forecasting by enhancing historical data

et al. 2022

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As an important part of renewable energy, wind power is crucial to the realization of carbon neutrality. It is worth studying on how to accurately predict the wind output so that it can be integrated into the power grid as much as possible to enhance its utilization rate. In this article, a data enhancement method and a framework are proposed to assist wind power forecasting. The proposed method uses the super-resolution perception technology to first detect the completeness and correctness of historical meteorological and wind power data collected by industrial devices. Then, the detected errors are corrected and the missing data are recovered to make the data complete. The frequency of the data is then increased using the proposed method so that the data become complete high-frequency data. Based on the enhanced complete high-frequency data with more detailed characteristics, more accurate forecasts of wind power can be achieved, thereby improving the utilization rate of wind power. Experiments based on public datasets are used to demonstrate the effectiveness of the proposed method and framework. With the proposed method and framework, higher frequency data with more detailed information can be achieved, thereby providing support for accurate wind power prediction that was not possible before.

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