Time Generative adversarial network for the generation of electricity load data

Aissa, Snani; Tarek, Khadir Mohamed

doi:10.1109/iccad57653.2023.10152457

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2024

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Cited by 2 publications

References 9 publications

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Harnessing GAN to Create Realistic FBS(False Base Station) Attack Data in 5G

Park,

Pawana,

Kim

et al. 2024

2024 IEEE International Symposium on Consumer Technology (ISCT)

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Harnessing GAN to Create Realistic FBS(False Base Station) Attack Data in 5G

Park,

Pawana,

Kim

et al. 2024

2024 IEEE International Symposium on Consumer Technology (ISCT)

View full text Add to dashboard Cite

Electricity Load Forecasting using Hybrid Datasets with Linear Interpolation and Synthetic Data

Dorji,

Jittanon,

Thanarak

et al. 2024

Eng. Technol. Appl. Sci. Res.

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Electricity load forecasting is an important aspect of power system management. Improving forecasting accuracy ensures reliable electricity supply, grid operations, and cost savings. Often, collected data consist of Missing Values (MVs), anomalies, outliers, or other inconsistencies caused by power failures, metering errors, data collection errors, hardware failures, network failures, or other unexpected events. This study uses real-world data to investigate the possibility of using synthetically generated data as an alternative to filling in MVs. Three datasets were created from an original one based on different imputation methods. The imputation methods employed were linear interpolation, imputation using synthetic data, and a proposed hybrid method based on linear interpolation and synthetic data. The performance of the three datasets was compared using deep learning, machine learning, and statistical models and verified based on forecasting accuracy improvements. The findings demonstrate that the hybrid dataset outperformed the other interpolation methods based on the forecasting accuracy of the models.

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Time Generative adversarial network for the generation of electricity load data

Cited by 2 publications

References 9 publications

Harnessing GAN to Create Realistic FBS(False Base Station) Attack Data in 5G

Harnessing GAN to Create Realistic FBS(False Base Station) Attack Data in 5G

Electricity Load Forecasting using Hybrid Datasets with Linear Interpolation and Synthetic Data

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