Performance assessment of five MCP models proposed for the estimation of long-term wind turbine power outputs at a target site using three machine learning techniques

Santiago Díaz, José A. Carta and José M. Matías

Applied Energy, 2018, vol. 209, issue C, 455-477

Abstract: Various models based on measure-correlate-predict (MCP) methods have been used to estimate the long-term wind turbine power output (WTPO) at target sites for which only short-term meteorological data are available. The MCP models used to date share the postulate that the influence of air density variation is of little importance, assume the standard value of 1.225 kg m−3 and only consider wind turbines (WTs) with blade pitch control.

Keywords: Support vector machine; Artificial neural network; Random forest; Wind turbine power curve; Wind turbine power output; Air density (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (18)

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DOI: 10.1016/j.apenergy.2017.11.007

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