A PV generation data reconstruction method based on improved super-resolution generative adversarial network

Zhang, Chengsheng; Shao, Zhenguo; Jiang, Changxu; Chen, Feixiong

doi:10.1016/j.ijepes.2021.107129

Cited by 10 publications

(3 citation statements)

References 35 publications

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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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show abstract

“…Lu and Jin [15] proposed to use a CNN and a GAN that respects the overall value in the prediction, Liu et al [16] proposed to use SRP to reconstruct missing values using a CNN called SRPCNN. Zhang et al [17] proposed to treat consumption as images and use a GAN called SRGAN to reconstruct the higher resolution load profile, Zhang et al [18] proposed a GAN called DISRGAN applied to photovoltaic plants treating the consumption as images, Ren et al [19] proposed a CNN to upsample low resolution sources into high resolution sources and Wang et al [20] proposed to apply a Graph CNN (GCN) for spatial-temporal convolutions by modeling consumption data as graphs in order to reconstruct higher resolutions from lower resolutions. Other non-linear modeling approaches that proved to be successful in modeling systems in various fields were proposed like Pozna and Precup [21], Zall and Kangavari [22], Hedrea et al [23]; also, the works of Ahmed et al [24], Precup et al [25], Yuhana et al [26] obtained good results in the topic.…”

Section: Related Workmentioning

confidence: 99%

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

Centeno¹,

García-Ordás

García-Olalla³

et al. 2021

Energies

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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.

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“…In recent years, data-driven generative models such as generative adversarial networks (GAN) (Goodfellow et al, 2014) have enabled the modeling of power systems without models. GAN was first introduced to renewable scenario generation in (Chen et al, 2018), and has been used in load generation (Wang et al, 2021), reconstruction of high-temporal-resolution PV generation data (Zhang et al, 2021), etc. Besides, GAN has also been introduced to generating electroencephalographic data (Debie et al, 2020), spatial-temporal data (Qu et al, 2020), and sensitive data in IIoT operations (Hindistan and Yetkin, 2023), etc.…”

Section: Introductionmentioning

confidence: 99%

A data-driven approach for generating load profiles based on InfoGAN and MKDE

Lan,

Zhou,

Guo

et al. 2023

Front. Energy Res.

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High-quality demand-side management requires an abundance of load profiles to support decision-making processes. However, customer energy consumption data often contains sensitive personal information, and service providers face significant challenges in accessing a substantial amount of energy consumption data. To generate a large volume of customer data without compromising privacy, this study introduces a data-driven approach integrating Information Maximizing Generative Adversarial Networks (InfoGAN) with Multivariate Kernel Density Estimation (MKDE) for the generation of load profiles. InfoGAN is firstly trained based on existing customer load profiles, with the Q network disentangling the load into feature variables and the generator producing realistic profiles. Subsequently, MKDE is utilized to assess the distribution of these features, enabling the generation of new profiles by sampling new feature variables. The proposed method circumvents the need for intricate sampling or modeling processes and generates realistic data that represents the inherent uncertainties and fluctuations characterizing customers’ electricity consumption. The generated data could be used as the substitution for real electricity consumption data, thereby facilitating further applications without compromising privacy concerns.

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A PV generation data reconstruction method based on improved super-resolution generative adversarial network

Cited by 10 publications

References 35 publications

Advancements in super-resolution methods for smart meter data

Advancements in super-resolution methods for smart meter data

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

A data-driven approach for generating load profiles based on InfoGAN and MKDE

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