Energy data generation with Wasserstein Deep Convolutional Generative Adversarial Networks
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DOI: 10.1016/j.energy.2022.124694
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- Haixia Gu & Gaojun Liu & Jixue Li & Hongyun Xie & Hanguan Wen, 2023. "A Framework Based on Deep Learning for Predicting Multiple Safety-Critical Parameter Trends in Nuclear Power Plants," Sustainability, MDPI, vol. 15(7), pages 1-15, April.
- Wen, Hanguan & Liu, Xiufeng & Yang, Ming & Lei, Bo & Xu, Cheng & Chen, Zhe, 2024. "A novel approach for identifying customer groups for personalized demand-side management services using household socio-demographic data," Energy, Elsevier, vol. 286(C).
- Chen, Zhiqiang & Li, Jianbin & Cheng, Long & Liu, Xiufeng, 2023. "Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation," Applied Energy, Elsevier, vol. 334(C).
- Yin, Linfei & Lin, Chen, 2024. "Matrix Wasserstein distance generative adversarial network with gradient penalty for fast low-carbon economic dispatch of novel power systems," Energy, Elsevier, vol. 298(C).
- Claeys, Robbert & Cleenwerck, Rémy & Knockaert, Jos & Desmet, Jan, 2024. "Capturing multiscale temporal dynamics in synthetic residential load profiles through Generative Adversarial Networks (GANs)," Applied Energy, Elsevier, vol. 360(C).
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Keywords
Privacy Protection; Sociodemographic Information; Generative Adversarial Network; Data Sharing;All these keywords.
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