Spatiotemporal Generative Adversarial Imputation Networks: An Approach to Address Missing Data for Wind Turbines

Hu, Xuguang; Zhan, Zhaokang; Ma, Dazhong; Zhang, Siqi

doi:10.1109/tim.2023.3312493

Cited by 9 publications

(1 citation statement)

References 34 publications

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“…The work presented in [14] achieved improved accuracy in missing imputation by employing clustering technology for pattern classification. The work presented in [15] proposes a spatiotemporal module to capture historical decay and feature correlation. The work in [16] enables dynamic adjustment of noise levels through the use of complete ensemble empirical mode decomposition with adaptive noise technology.…”

Section: Missing Imputation Model Based On Generative Adversarial Net...mentioning

confidence: 99%

Denoising Masked Autoencoder-Based Missing Imputation within Constrained Environments for Electric Load Data

Jeong,

Ku,

Park

2023

Energies

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With recent advancements in data technologies, particularly machine learning, research focusing on the enhancement of energy efficiency in residential, commercial, and industrial settings through the collection of load data, such as heat, electricity, and gas, has gained significant attention. Nevertheless, issues arising from hardware- or network-related problems can result in missing data, necessitating the development of management techniques to mitigate these challenges. Traditional methods for missing imputation face difficulties when operating in constrained environments characterized by short data collection periods and frequent consecutive missing. In this paper, we introduce the denoising masked autoencoder (DMAE) model as a solution to improve the handling of missing data, even in such restrictive settings. The proposed DMAE model capitalizes on the advantages of the denoising autoencoder (DAE), enabling effective learning of the missing imputation process, even with relatively small datasets, and the masked autoencoder (MAE), allowing for learning in environments with a high missing ratio. By integrating these strengths, the DMAE model achieves an enhanced performance in terms of missing imputation. The simulation results demonstrate that the proposed DMAE model outperforms the DAE or MAE significantly in a constrained environment where the duration of the training data is short, less than a year, and missing values occur frequently with durations ranging from 3 h to 12 h.

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Section: Missing Imputation Model Based On Generative Adversarial Net...mentioning

confidence: 99%

Denoising Masked Autoencoder-Based Missing Imputation within Constrained Environments for Electric Load Data

Jeong,

Ku,

Park

2023

Energies

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CC-GAIN: Clustering and classification-based generative adversarial imputation network for missing electricity consumption data imputation

Hwang,

Suh

2024

Expert Systems with Applications

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Enhancement of Texas wind turbine power predictions using fractional order neural network by incorporating machine learning models to impute missing data

Ramadevi,

Kasi,

Bingi

2024

Knowledge-Based Systems

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Spatiotemporal Generative Adversarial Imputation Networks: An Approach to Address Missing Data for Wind Turbines

Cited by 9 publications

References 34 publications

Denoising Masked Autoencoder-Based Missing Imputation within Constrained Environments for Electric Load Data

Denoising Masked Autoencoder-Based Missing Imputation within Constrained Environments for Electric Load Data

CC-GAIN: Clustering and classification-based generative adversarial imputation network for missing electricity consumption data imputation

Enhancement of Texas wind turbine power predictions using fractional order neural network by incorporating machine learning models to impute missing data

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