Reply to ‘The perils of automated fitting of datasets: the case of a wind turbine cost model’

Erkka Rinne

Reply to ‘The perils of automated fitting of datasets: the case of a wind turbine cost model’

Erkka Rinne

Research output: Contribution to journal › Article › Scientific

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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.

Original language	English
Journal	Nature Energy
Publication status	Submitted - 13 Aug 2019
MoE publication type	B1 Article in a scientific magazine

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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NENERGY-19071342_replySubmitted manuscript, 292 KBLicence: CC BY

1 Article

Effects of turbine technology and land use on wind power resource potential
Rinne, E., Holttinen, H., Kiviluoma, J. & Rissanen, S., 14 May 2018, In: Nature Energy. 3, 6, p. 494-500
Research output: Contribution to journal › Article › Scientific › peer-review

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Cite this

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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.",

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AB - 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.

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SN - 2058-7546

JO - Nature Energy

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ER -

Reply to ‘The perils of automated fitting of datasets: the case of a wind turbine cost model’

Abstract

UN SDGs

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Research output

Effects of turbine technology and land use on wind power resource potential

Cite this