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.

A performance assessment is undertaken in this paper of the models used to date and of newly proposed models. Our models incorporate air density in the MCP model as an additional covariable in long-term WTPO estimation and consider both WTs with blade pitch control and stall-regulated WTs. The advantages of including this covariable are assessed using different functional forms and different machine learning algorithms for their implementation (Artificial Neural Network, Support Vector Machine for regression and Random Forest).

The models and the regression techniques used in them were applied to the mean hourly wind speeds and directions and air densities recorded in 2014 at ten weather stations in the Canary Archipelago (Spain). Several conclusions were drawn from the results, including most notably: (a) to clearly show the notable effect of air density variability when estimating WTPOs, it is important to consider the functional ways in which the features air density and wind speed and direction intervene, (b) of the five MCP models under comparison, the one that separately estimates wind speeds and air densities to later predict the WTPOs always provided the best mean absolute error, mean absolute relative error and coefficient of determination metrics, independently of the target station and type of WT under consideration, (c) the models which used Support Vector Machines (SVMs) for regression or random forests (RFs) always provided better metrics than those that used artificial neural networks, with the differences being statistically significant (5% significance) for most of the cases assessed, (d) no statistically significant differences were found between the SVM- and RF-based models.

基于测度-相关-预测（MCP）方法的各种模型已用于估算仅短期气象数据可用的目标地点的长期风力涡轮机功率输出（WTPO）。迄今为止使用的MCP模型假设空气密度变化的影响并不重要，假设标准值为1.225 kg m ^-3，并且仅考虑具有桨距控制的风力涡轮机（WTs）。

本文对迄今为止使用的模型和新提出的模型进行了性能评估。我们的模型将空气密度纳入MCP模型中，作为长期WTPO估算中的附加协变量，并同时考虑了具有桨距控制的WT和失速调节的WT。使用不同的函数形式和不同的机器学习算法（包括人工神经网络，用于回归的支持向量机和随机森林）来评估包含此协变量的优势。

模型中使用的模型和回归技术应用于2014年在加那利群岛（西班牙）的十个气象站记录的平均每小时风速，风向和空气密度。从结果中得出了几个结论，其中最引人注目的是：（a）为了清楚地显示出估算WTPO时空气密度变化的显着影响，重要的是要考虑空气密度，风速和风向进行干预的功能方式， （b）在进行比较的5个MCP模型中，分别估算风速和空气密度以随后预测WTPO的模型始终独立于目标台站和目标站提供了最佳的平均绝对误差，平均绝对相对误差和确定系数。正在考虑的WT类型，