创新型变压器神经网络用于在不同轮机枢纽高度的风密度函数估计
Amit Kumar Yadav1, Vibha Yadav2, Walle Tilahun3
1School of Computer Science and Artificial Intelligence, SR University, Warangal, Telangana, 506371, India.
Scientific reports
|July 2, 2025
概括
变压器神经网络 (TNN) 通过分析不同高度的风速模式,准确地估计风能潜力. 这种方法为风能资源评估提供了可靠的工具,改进了传统的韦布尔分布功能.
科学领域:
- 可再生能源可再生能源是可再生能源.
- 人工智能的人工智能
- 气象学 天气学
背景情况:
- 精确的风力发电潜力 (WPP) 估计对于风力轮机安装和资源评估至关重要.
- 风速 (WS) 随着轮枢纽高度的变化而变化,用韦布尔分布函数 (WDF) 创建复杂的非线性方程.
- 传统的WDF方法在准确建模高度依赖的风速变化方面面临挑战.
研究的目的:
- 引入创新的变压器神经网络 (TNN) 模型用于风密度估计 (WDE).
- 利用TNN中的自我注意机制来捕捉风速数据中的复杂模式.
- 评估印度东北部80米高的WPP,这是该地区的新高度范围.
主要方法:
- 为WDE开发了一个变压器神经网络 (TNN) 模型.
- 使用立方系数 (CF) 方法来评估韦布尔参数 (尺度"c"和形状"k").
- 通过使用诸如MSE,RMSE,MAE,R2,MBE和MAPE等指标,严格评估了TNN的表现.
主要成果:
- 发现立方系数 (CF) 和形状参数 (k) 独立于高度 (分别为1.96和1.95).
- 尺度参数"c"在10米至150米高处从2.65到3.90不等.
- TNN模型在估计WDF方面表现出很高的准确性,CDF的R2为0.9170,WDF的R2为0.9039.
结论:
- 变压器神经网络 (TNN) 模型被证明是估计风密度函数 (WDF) 的高度准确和强大的工具.
- TNN模型有效地捕捉了不同高度的风速变化的复杂模式.
- 这种创新方法提供了一种可靠的方法,用于评估各种轮机枢纽高度的风能潜力.
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