风速预测

Alvin Xianghan Li
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引用次数: 0

摘要

在风力发电场的帮助下,风能成为一种重要的可再生能源,为世界能源平衡做出了巨大贡献。有了准确的风速预测,风力涡轮机的使用寿命和维护成本就会降低。另一方面,风速具有高度不稳定性和不可预测性。因此,必须开展研究,建立复杂的模型和算法,以进行精确的风速预测。到目前为止,最有前途的模型包括支持向量机(SVM)、人工神经网络(ANN)和自回归移动平均(ARMA)。Python 作为一种先进的通用编程语言,非常适合编写这些复杂模型的算法脚本。本文将使用德克萨斯州奥斯汀的数据,并应用支持向量机(SVM)进行风速预测,包括数据收集、数据预处理、模型选择、模型训练、参数优化、模型验证和预测等几个阶段。通过将这些模型纳入预测分析,并根据不断变化的数据对其进行持续改进,风能资源优化、维护成本降低和风电场总效率都能得到显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wind speed prediction
With the help of wind farms, wind energy is a vital renewable energy source that contributes significantly to the worlds energy balance. The lifespan and maintenance costs of wind turbines will be reduced with an accurate wind speed prediction. On the other hand, wind speed is highly volatile and unpredictable. Thus, it is essential to do research into creating complex models and algorithms for precise wind speed prediction. So far, some of the most promising models include Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Autoregressive Moving Average (ARMA). Python, as an advanced and versatile programming language, is exceptionally suited for scripting the algorithms of these sophisticated models. This paper will use the data from Austin Texas and apply a Support Vector Machine (SVM) for wind speed prediction involves several stages, including data collection, data preprocessing, model selection, model training, parameter optimization, model validation, and prediction. Wind energy resource optimisation, maintenance cost reduction, and total wind farm efficiency can all be significantly improved by incorporating these models into predictive analytics and continuously improving them against changing data.
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