使用多变量自适应回归线和回归树进行多变量风力发电曲线建模
Khurram Mushtaq1, Runmin Zou1, Asim Waris2
1School of Automation, Central South University, Changsha, China.
PloS one
|August 28, 2023
概括
这项研究通过使用多变量数据和MARS技术来增强风力轮机功率曲线 (WTPC) 建模. 这些方法提高了准确性,并有效处理异常数据,以便更好地预测风力发电和状态监测.
科学领域:
- 可再生能源工程可再生能源工程
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 风力轮机功率曲线 (WTPC) 对于监测和预测至关重要.
- 现有的WTPC模型与复杂的环境因素,技术问题和数据异常值作斗争,限制了准确性.
- 预处理技术不足以解决固有的数据不一致性.
研究的目的:
- 为了提高风力轮机功率曲线 (WTPC) 模型的准确性.
- 解决模拟复杂非线性关系和处理异常数据的局限性.
- 开发一个强大的WTPC建模技术,用于增强风能评估.
主要方法:
- 开发包含额外输入变量的多变量WTPC模型.
- 多变量自适应回归支柱 (MARS) 的应用,用于灵活的非线性建模.
- 基于错误分布分析的新型异常值检测方法的实施.
主要成果:
- 与单变量模型相比,多变量模型显著提高了功率曲线估计的准确性.
- 马斯展示了卓越的非线性合适能力,超过了回归树和其他方法.
- 提出的方法有效地减轻了隐藏的异常值的不利影响,从而导致更接近的错误分布.
结论:
- 多变量建模和MARS是提高WTPC准确性的有效技术.
- 开发的异常值检测方法成功地识别和解决了WTPC数据中隐藏的异常值.
- 这项研究为风力轮机状况监测和功率预测提供了更可靠的方法.
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