在斐济的一个地点使用时间序列,回归和ANN技术,测量波浪能量和预测功率的时间变化
Avikesh Kumar1, Gabiriele Bulivou1, Mohammed Rafiuddin Ahmed1
1School of Information Technology, Engineering, Mathematics & Physics, The University of the South Pacific, Suva, Fiji.
Journal of the Royal Society of New Zealand
|May 8, 2025
概括
这项研究使用人工智能 (AI) 模型预测波浪能量,发现它比传统方法更准确. 准确的波浪能源预测对于最大限度地利用可再生能源发电至关重要.
科学领域:
- 可再生能源系统可再生能源系统
- 气候变化缓解缓解 气候变化缓解
- 海洋学 海洋学 海洋学
背景情况:
- 全球能源转型向清洁,可靠的能源.
- 波浪能源作为可行的可再生能源替代品.
- 需要准确的波浪能量预测.
研究的目的:
- 开发和比较波浪能源预测模型.
- 评估人工神经网络 (ANN) 模型的性能与统计方法相比.
- 通过精确的建模,优化波浪能量的产生.
主要方法:
- 人工神经网络 (ANN) 模型开发.
- 时间序列和回归模型的应用.
- 使用MSE,RMSE,MAE,MAPE和R2进行绩效评估.
- 与一个天真的模型进行基准测试.
主要成果:
- 安恩模型表现出卓越的准确性和效率.
- 在波能预测方面,ANN的表现优于回归和时间序列模型.
- 波浪高度和周期被确定为关键的独立变量.
结论:
- 对于准确的波浪能量预测,ANN模型非常有效.
- 准确的预测可以通过阻抗匹配实现最大限度的发电.
- 波浪能源预测支持可持续的能源解决方案.
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