一个双重的GAN,具有身份封锁和胰腺启发的损失,用于可再生能源优化
Mostafa Elbaz1, Wael Said2, Gamal M Mahmoud3
1Department of Computer Science, Faculty of Computers and Informatics, Kafrelsheikh University, Kafrelsheikh, Egypt. mostafa.albaz@fci.kfs.edu.eg.
Scientific reports
|May 13, 2025
概括
卡-GAN产生高质量的合成可再生能源图像,克服了传统的生成对抗网络 (GAN) 的局限性. 这提高了太阳能和风能应用中的预测模型和故障检测准确度.
科学领域:
- 可再生能源工程可再生能源工程
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 高质量的数据集对于准确的可再生能源预测和故障检测至关重要.
- 传统的生成对抗网络 (GAN) 面临模式崩和像素完整性问题等挑战,限制合成数据生成.
- 高质量的数据集的有限可用性阻碍了可再生能源应用中的预测模型的准确性.
研究的目的:
- 引入 Penca-GAN,这是一种新的生成对抗网络 (GAN) 架构,旨在生成可再生能源应用的高保真合成图像.
- 解决传统GAN的局限性,包括模式崩,消失梯度和像素完整性问题.
- 提高可再生能源领域预测模型和故障检测系统的性能.
主要方法:
- 开发了Penca-GAN,结合了像素完整性和多样性的双损失函数,用于训练稳定的身份块,以及胰腺启发的元启发性损失函数.
- 在SKY,太阳能和风力轮机图像数据集上进行了广泛的实验.
- 使用Fréchet Inception Distance (FID) 和Inception Score (IS) 评估Penca-GAN的性能,并评估其对故障检测准确性的影响.
主要成果:
- 在所有数据集 (SKY: 164.45,太阳能: 113.54,风力轮机:109.34) 中,Penca-GAN获得了最低的Fréchet Inception Distance (FID) 评分,这表明合成图像质量优越.
- 在所有数据集中 (SKY: 71.43,太阳能: 87.65,风力轮机: 90.32) 获得最高的初始分数 (IS).
- 显著提高故障检测准确度:太阳能电池板的故障检测准确度从85.92%提高到90.04%,风力轮机的故障检测准确度从86.06%提高到90.43%.
结论:
- 卡-GAN有效地产生高保真度合成图像,克服了传统GAN的局限性.
- 新型架构显著提高了可再生能源应用的性能,特别是在故障检测和能源预测方面.
- Penca-GAN代表了对关键能源部门任务的合成数据生成的实质性进展.
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