## 4 Data Description and Preparation  
The SCADA data-based condition/performance monitoring is a cost-effective approach as it provides crucial information regarding the load history and operations of individual turbines. They provide an efficient tool for continuous CM of a turbine for early warning of failures and related performance issues. The SCADA data points collected from the Whitelee WF (located in Scotland, UK) are used for training and validating the proposed models, including data pre-processing, air density correction, and model evaluation. The Whitelee WF is located to the south of Glasgow, Scotland and amounts to 215 Siemens and Alstom onshore WTs with a total installed capacity of 539 MW. They record more than 100 different signals such as electrical (e.g., real and reactive power output and currents and voltages in the generator windings); weather-related signals (e.g., anemometer-measured hub height wind speed and direction, and ambient temperature); various temperature signals, such as main bearing and gearbox; pitch information (e.g., set and actual blade angles); numerous other signals. All this information is in the form of maximum, minimum, average and standard deviation values, with a 10 min average value. Measured wind speed is the most significant source of uncertainty and including more data points gives more certainty to the average value in the WT power curve. Type B uncertainties would be challenging to treat in a consistent manner without greater knowledge of the instrumentation used. Therefore, in this paper, we used the statistical spread for the SVM-based power curve using CIs. The data points used in this study cover the period from 00:00 on 1 st September 2012 to 23:50 on 30 th November 2012, accounting for 13,250 data samples in total. Recorded data can be imperfect due to sensor failures or malfunctions; these need to be removed or corrected as they affect the accuracy of any proposed models. Firstly, samples with missing values or negative power values are filtered out. Data points where maximum wind speed has reached more than 20 m/s are also filtered out because beyond this wind speed the turbine is stopped. In addition, data sampling during frequent start-up or stop in the low-wind-speed period may have a different variation. In short, criterion such as timestamp mismatch, negative power values, out of range values and turbine curtailment are used to filter out such misleading data similar to the one described in [13; 23]. This reduces the number of data samples to 7918. Once the data are pre-processed, the next step is to carry out data partition into training and testing to make the model robust. Two problems need to be discussed when conducting data partition; with fewer training data, estimated values of the SVM model will have more significant variance while estimated values output statistics will have more substantial variance with less testing data. Thus, to guarantee variation within reasonable limits, a balance between training and testing data points is required. It should be noted that the inclusion of extensive training data improves data-driven models, such as SVM performance. In contrast, more testing data help in estimating errors accurately. Therefore, many studies [32; 33; 34] suggested that 70% training and 30% testing partition size are found to give the right combination of accuracy and precision. Hence, filtered SCADA data points are randomly divided into training and testing subsets in the ratio of 70:30, i.e., 5542 for training and 2376 for evaluation, as illustrated in Table 1. The SVM model has never seen the testing data points, so the resulting outcome will be an excellent guide to analyse its impact on power curve accuracy and uncertainty when the model is applied to unseen data and this is discussed in the sections below.