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Abstract
Assessing the investment costs of wind power plants with different technological
parameters is a challenging task. Previously, a cost model to predict specific investment costs using chosen technology parameters was created by fitting a function to cost data. The model, however, shows incorrect scaling behaviour with large installed capacity. A new model was tested with the original data, but it is difficult to estimate its quality without an extensive analysis.
parameters is a challenging task. Previously, a cost model to predict specific investment costs using chosen technology parameters was created by fitting a function to cost data. The model, however, shows incorrect scaling behaviour with large installed capacity. A new model was tested with the original data, but it is difficult to estimate its quality without an extensive analysis.
| Original language | English |
|---|---|
| Journal | Nature Energy |
| Publication status | Submitted - 13 Aug 2019 |
| MoE publication type | B1 Article in a scientific magazine |
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Dive into the research topics of 'Reply to ‘The perils of automated fitting of datasets: the case of a wind turbine cost model’'. Together they form a unique fingerprint.Research output
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Effects of turbine technology and land use on wind power resource potential
Rinne, E. (Corresponding Author), Holttinen, H., Kiviluoma, J. & Rissanen, S., 14 May 2018, In: Nature Energy. 3, 6, p. 494-500Research output: Contribution to journal › Article › Scientific › peer-review
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