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

## TL;DR

This paper examines the risks of automated regression analysis in modeling wind turbine costs, highlighting a specific flawed model and its implications for generalization and application in wind energy potential assessments.

## Contribution

The paper presents a detailed model of site-specific wind turbine investment costs and critically analyzes the flaws in the site-independent cost model, illustrating potential pitfalls of automated fitting.

## Key findings

- Site-specific cost model is detailed and innovative.
- Site-independent cost model has a significant flaw.
- Automated regression can lead to unreasonable results in this context.

## Abstract

Rinne et al. conduct an interesting analysis of the impact of wind turbine technology and land-use on wind power potentials, which allows profound insights into each factors contribution to overall potentials. The paper presents a detailed 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. We believe that propose a cutting edge model of site-specific investment costs. However, the site-independent cost model is flawed in our opinion. This flaw most likely does not impact the results presented in the paper, although 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.