The perils of automated fitting of datasets: The case of a wind turbine cost model

Claude Klöckl, Katharina Gruber, Peter Regner, Sebastian Wehrle, Johannes Schmidt
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引用次数: 1

Abstract

Rinne et al. (2018) conduct a detailed analysis of the impact of wind turbine technology and land-use on wind power potentials, which allows important insights into each factor’s contribution to overall potentials. The paper presents a detailed and very valuable model of site-specific wind turbine investment cost (i.e. road- and grid access costs), complemented by a model used to estimate site-independent costs.

However, the site-independent cost model is flawed in our opinion. This flaw most likely does not impact the results on cost supply-curves of wind power presented in the paper. However, we expect a considerable generalization error. Thus the application of the wind turbine cost model in other contexts may lead to unreasonable results. More generally, the derivation of the wind turbine cost model serves as an example of how applications of automated regression analysis can go wrong.

自动拟合数据集的危险:以风力涡轮机成本模型为例
Rinne等人。(2018)对风力涡轮机技术和土地使用对风力发电潜力的影响进行了详细分析,从而可以深入了解每个因素对整体潜力的贡献。本文提出了一个详细且非常有价值的现场风机投资成本模型(即道路和电网接入成本),并辅以一个用于估计现场独立成本的模型。然而,在我们看来,独立于站点的成本模型是有缺陷的。这一缺陷很可能不会影响本文提出的风电成本供应曲线的结果。然而,我们预计会出现相当大的泛化错误。因此,在其他情况下应用风力涡轮机成本模型可能会导致不合理的结果。更一般地说,风力涡轮机成本模型的推导是自动回归分析的应用如何出错的一个例子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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